references.bib

@MISC{European_Organization_for_Nuclear_Research2013-uf,
  title     = "Zenodo",
  author    = "{European Organization for Nuclear Research} and {OpenAIRE}",
  abstract  = "Zenodo is a general purpose repository that enables researchers,
               scientists, projects and institutions to share, preserve and
               showcase multidisciplinary research results (data, software and
               publications) that are not part of the existing institutional or
               subject-based repositories of the research communities. It is
               founded in the trustworthy CERN data centre.",
  publisher = "CERN",
  year      =  2013
}

@ARTICLE{Brown2007-ou,
  title    = "Fluorescence microscopy--avoiding the pitfalls",
  author   = "Brown, Claire M",
  journal  = "J. Cell Sci.",
  volume   =  120,
  number   = "Pt 10",
  pages    = "1703--1705",
  month    =  may,
  year     =  2007,
  language = "en"
}

@MISC{noauthor_undated-tk,
  title        = "{OSF}",
  howpublished = "\url{https://osf.io/}",
  note         = "Accessed: 2022-12-14",
  language     = "en"
}

@ARTICLE{Culley2023-dj,
  title         = "Made to measure: an introduction to quantification in
                   microscopy data",
  author        = "Culley, Si{\^a}n and Caballero, Alicia Cuber and Burden,
                   Jemima J and Uhlmann, Virginie",
  abstract      = "Images are at the core of most modern biological experiments
                   and are used as a major source of quantitative information.
                   Numerous algorithms are available to process images and make
                   them more amenable to be measured. Yet the nature of the
                   quantitative output that is useful for a given biological
                   experiment is uniquely dependent upon the question being
                   investigated. Here, we discuss the 3 main types of visual
                   information that can be extracted from microscopy data:
                   intensity, morphology, and object counts or categorical
                   labels. For each, we describe where they come from, how they
                   can be measured, and what may affect the relevance of these
                   measurements in downstream data analysis. Acknowledging that
                   what makes a measurement ``good'' is ultimately down to the
                   biological question being investigated, this review aims at
                   providing readers with a toolkit to challenge how they
                   quantify their own data and be critical of conclusions drawn
                   from quantitative bioimage analysis experiments.",
  month         =  feb,
  year          =  2023,
  archivePrefix = "arXiv",
  journal  = "arxiv",
  primaryClass  = "q-bio.QM",
  eprint        = "2302.01657"
}

@ARTICLE{Pittet2011-dn,
  title    = "Intravital imaging",
  author   = "Pittet, Mikael J and Weissleder, Ralph",
  abstract = "Until recently, the idea of observing life deep within the
              tissues of a living mouse, at a resolution sufficient to pick out
              cellular behaviors and molecular signals underlying them,
              remained a much-coveted dream. Now, a new era of intravital
              fluorescence microscopy has dawned. In this Primer, we review the
              technologies that made this revolution possible and demonstrate
              how intravital imaging is beginning to provide quantitative and
              dynamic insights into cell biology, immunology, tumor biology,
              and neurobiology.",
  journal  = "Cell",
  volume   =  147,
  number   =  5,
  pages    = "983--991",
  month    =  nov,
  year     =  2011,
  language = "en"
}

@ARTICLE{Ellenberg2018-co,
  title    = "A call for public archives for biological image data",
  author   = "Ellenberg, Jan and Swedlow, Jason R and Barlow, Mary and Cook,
              Charles E and Sarkans, Ugis and Patwardhan, Ardan and Brazma,
              Alvis and Birney, Ewan",
  abstract = "Public data archives are the backbone of modern biological
              research. Biomolecular archives are well established, but
              bioimaging resources lag behind them. The technology required for
              imaging archives is now available, thus enabling the creation of
              the first public bioimage datasets. We present the rationale for
              the construction of bioimage archives and their associated
              databases to underpin the next revolution in bioinformatics
              discovery.",
  journal  = "Nat. Methods",
  volume   =  15,
  number   =  11,
  pages    = "849--854",
  month    =  nov,
  year     =  2018,
  language = "en"
}

@ARTICLE{Soneson2014-hw,
  title    = "Batch effect confounding leads to strong bias in performance
              estimates obtained by cross-validation",
  author   = "Soneson, Charlotte and Gerster, Sarah and Delorenzi, Mauro",
  abstract = "BACKGROUND: With the large amount of biological data that is
              currently publicly available, many investigators combine multiple
              data sets to increase the sample size and potentially also the
              power of their analyses. However, technical differences (``batch
              effects'') as well as differences in sample composition between
              the data sets may significantly affect the ability to draw
              generalizable conclusions from such studies. FOCUS: The current
              study focuses on the construction of classifiers, and the use of
              cross-validation to estimate their performance. In particular, we
              investigate the impact of batch effects and differences in sample
              composition between batches on the accuracy of the classification
              performance estimate obtained via cross-validation. The focus on
              estimation bias is a main difference compared to previous
              studies, which have mostly focused on the predictive performance
              and how it relates to the presence of batch effects. DATA: We
              work on simulated data sets. To have realistic intensity
              distributions, we use real gene expression data as the basis for
              our simulation. Random samples from this expression matrix are
              selected and assigned to group 1 (e.g., 'control') or group 2
              (e.g., 'treated'). We introduce batch effects and select some
              features to be differentially expressed between the two groups.
              We consider several scenarios for our study, most importantly
              different levels of confounding between groups and batch effects.
              METHODS: We focus on well-known classifiers: logistic regression,
              Support Vector Machines (SVM), k-nearest neighbors (kNN) and
              Random Forests (RF). Feature selection is performed with the
              Wilcoxon test or the lasso. Parameter tuning and feature
              selection, as well as the estimation of the prediction
              performance of each classifier, is performed within a nested
              cross-validation scheme. The estimated classification performance
              is then compared to what is obtained when applying the classifier
              to independent data.",
  journal  = "PLoS One",
  volume   =  9,
  number   =  6,
  pages    = "e100335",
  month    =  jun,
  year     =  2014,
  language = "en"
}

@ARTICLE{Hartley2022-mt,
  title    = "The {BioImage} Archive - Building a Home for {Life-Sciences}
              Microscopy Data",
  author   = "Hartley, Matthew and Kleywegt, Gerard J and Patwardhan, Ardan and
              Sarkans, Ugis and Swedlow, Jason R and Brazma, Alvis",
  abstract = "Despite the huge impact of data resources in genomics and
              structural biology, until now there has been no central archive
              for biological data for all imaging modalities. The BioImage
              Archive is a new data resource at the European Bioinformatics
              Institute (EMBL-EBI) designed to fill this gap. In its initial
              development BioImage Archive accepts bioimaging data associated
              with publications, in any format, from any imaging modality from
              the molecular to the organism scale, excluding medical imaging.
              The BioImage Archive will ensure reproducibility of published
              studies that derive results from image data and reduce
              duplication of effort. Most importantly, the BioImage Archive
              will help scientists to generate new insights through reuse of
              existing data to answer new biological questions, and provision
              of training, testing and benchmarking data for development of
              tools for image analysis. The archive is available at
              https://www.ebi.ac.uk/bioimage-archive/.",
  journal  = "J. Mol. Biol.",
  volume   =  434,
  number   =  11,
  pages    = "167505",
  month    =  jun,
  year     =  2022,
  keywords = "FAIR; database; imaging; microscopy; open",
  language = "en"
}

@ARTICLE{Icha2017-jf,
  title    = "Phototoxicity in live fluorescence microscopy, and how to avoid
              it",
  author   = "Icha, Jaroslav and Weber, Michael and Waters, Jennifer C and
              Norden, Caren",
  abstract = "Phototoxicity frequently occurs during live fluorescence
              microscopy, and its consequences are often underestimated. Damage
              to cellular macromolecules upon excitation light illumination can
              impair sample physiology, and even lead to sample death. In this
              review, we explain how phototoxicity influences live samples, and
              we highlight that, besides the obvious effects of phototoxicity,
              there are often subtler consequences of illumination that are
              imperceptible when only the morphology of samples is examined.
              Such less apparent manifestations of phototoxicity are equally
              problematic, and can change the conclusions drawn from an
              experiment. Thus, limiting phototoxicity is a prerequisite for
              obtaining reproducible quantitative data on biological processes.
              We present strategies to reduce phototoxicity, e.g. limiting the
              illumination to the focal plane and suggest controls for
              phototoxicity effects. Overall, we argue that phototoxicity needs
              increased attention from researchers when designing experiments,
              and when evaluating research findings.",
  journal  = "Bioessays",
  volume   =  39,
  number   =  8,
  month    =  aug,
  year     =  2017,
  keywords = "light sheet fluorescence microscopy; live imaging;
              photobleaching; photodamage; phototoxicity; reactive oxygen
              species; selective plane illumination",
  language = "en"
}

@ARTICLE{Li2015-ys,
  title    = "Paraformaldehyde Fixation May Lead to Misinterpretation of the
              Subcellular Localization of Plant High Mobility Group Box
              Proteins",
  author   = "Li, Man-Wah and Zhou, Liang and Lam, Hon-Ming",
  abstract = "Arabidopsis High Mobility Group Box (HMBG) proteins were
              previously found associated with the interphase chromatin but not
              the metaphase chromosome. However, these studies are usually
              based on immunolocalization analysis involving paraformaldehyde
              fixation. Paraformaldehyde fixation has been widely adapted to
              preserved cell morphology before immunofluorescence staining. On
              one hand, the processed cells are no longer living. On the other
              hand, the processing may lead to misinterpretation of
              localization. HMGBs from Arabidopsis were fused with enhanced
              green fluorescence protein (EGFP) and transformed into tobacco
              BY-2 cells. Basically, the localization of these HMGB proteins
              detected with EGFP fluorescence in interphase agreed with
              previous publications. Upon 4\% paraformaldehyde fixation,
              AtHMGB1 was found associated with interphase but not the
              metaphase chromosomes as previously reported. However, when EGFP
              fluorescence signal was directly observed under confocal
              microscope without fixation, association of AtHMGB1 with
              metaphase chromosomes can be detected. Paraformaldehyde fixation
              led to dissociation of EGFP tagged AtHMBG1 protein from metaphase
              chromosomes. This kind of pre-processing of live specimen may
              lead to dissociation of protein-protein or protein-nucleic acid
              interaction. Therefore, using of EGFP fusion proteins in live
              specimen is a better way to determine the correct localization
              and interaction of proteins.",
  journal  = "PLoS One",
  volume   =  10,
  number   =  8,
  pages    = "e0135033",
  month    =  aug,
  year     =  2015,
  language = "en"
}

@ARTICLE{Schindelin2012-kk,
  title    = "Fiji: an open-source platform for biological-image analysis",
  author   = "Schindelin, Johannes and Arganda-Carreras, Ignacio and Frise,
              Erwin and Kaynig, Verena and Longair, Mark and Pietzsch, Tobias
              and Preibisch, Stephan and Rueden, Curtis and Saalfeld, Stephan
              and Schmid, Benjamin and Tinevez, Jean-Yves and White, Daniel
              James and Hartenstein, Volker and Eliceiri, Kevin and Tomancak,
              Pavel and Cardona, Albert",
  abstract = "Fiji is a distribution of the popular open-source software ImageJ
              focused on biological-image analysis. Fiji uses modern software
              engineering practices to combine powerful software libraries with
              a broad range of scripting languages to enable rapid prototyping
              of image-processing algorithms. Fiji facilitates the
              transformation of new algorithms into ImageJ plugins that can be
              shared with end users through an integrated update system. We
              propose Fiji as a platform for productive collaboration between
              computer science and biology research communities.",
  journal  = "Nat. Methods",
  volume   =  9,
  number   =  7,
  pages    = "676--682",
  month    =  jun,
  year     =  2012,
  language = "en"
}

@ARTICLE{Gomez-Gaviro2020-jq,
  title    = "Biomedical Applications of Tissue Clearing and
              {Three-Dimensional} Imaging in Health and Disease",
  author   = "G{\'o}mez-Gaviro, Maria Victoria and Sanderson, Daniel and
              Ripoll, Jorge and Desco, Manuel",
  abstract = "Three-dimensional (3D) optical imaging techniques can expand our
              knowledge about physiological and pathological processes that
              cannot be fully understood with 2D approaches. Standard
              diagnostic tests frequently are not sufficient to unequivocally
              determine the presence of a pathological condition. Whole-organ
              optical imaging requires tissue transparency, which can be
              achieved by using tissue clearing procedures enabling deeper
              image acquisition and therefore making possible the analysis of
              large-scale biological tissue samples. Here, we review currently
              available clearing agents, methods, and their application in
              imaging of physiological or pathological conditions in different
              animal and human organs. We also compare different optical tissue
              clearing methods discussing their advantages and disadvantages
              and review the use of different 3D imaging techniques for the
              visualization and image acquisition of cleared tissues. The use
              of optical tissue clearing resources for large-scale biological
              tissues 3D imaging paves the way for future applications in
              translational and clinical research.",
  journal  = "iScience",
  volume   =  23,
  number   =  8,
  pages    = "101432",
  month    =  aug,
  year     =  2020,
  keywords = "Biomedical Discipline; Imaging Methods in Chemistry; Medical
              Imaging; Optical Imaging",
  language = "en"
}

@ARTICLE{Stirling2021-sg,
  title    = "{CellProfiler} 4: improvements in speed, utility and usability",
  author   = "Stirling, David R and Swain-Bowden, Madison J and Lucas, Alice M
              and Carpenter, Anne E and Cimini, Beth A and Goodman, Allen",
  abstract = "BACKGROUND: Imaging data contains a substantial amount of
              information which can be difficult to evaluate by eye. With the
              expansion of high throughput microscopy methodologies producing
              increasingly large datasets, automated and objective analysis of
              the resulting images is essential to effectively extract
              biological information from this data. CellProfiler is a free,
              open source image analysis program which enables researchers to
              generate modular pipelines with which to process microscopy
              images into interpretable measurements. RESULTS: Herein we
              describe CellProfiler 4, a new version of this software with
              expanded functionality. Based on user feedback, we have made
              several user interface refinements to improve the usability of
              the software. We introduced new modules to expand the
              capabilities of the software. We also evaluated performance and
              made targeted optimizations to reduce the time and cost
              associated with running common large-scale analysis pipelines.
              CONCLUSIONS: CellProfiler 4 provides significantly improved
              performance in complex workflows compared to previous versions.
              This release will ensure that researchers will have continued
              access to CellProfiler's powerful computational tools in the
              coming years.",
  journal  = "BMC Bioinformatics",
  volume   =  22,
  number   =  1,
  pages    = "433",
  month    =  sep,
  year     =  2021,
  keywords = "Bioimaging; Image analysis; Image quantitation; Image
              segmentation; Microscopy",
  language = "en"
}

@ARTICLE{Wan2019-rv,
  title    = "{Light-Sheet} Microscopy and Its Potential for Understanding
              Developmental Processes",
  author   = "Wan, Yinan and McDole, Katie and Keller, Philipp J",
  abstract = "The ability to visualize and quantitatively measure dynamic
              biological processes in vivo and at high spatiotemporal
              resolution is of fundamental importance to experimental
              investigations in developmental biology. Light-sheet microscopy
              is particularly well suited to providing such data, since it
              offers exceptionally high imaging speed and good spatial
              resolution while minimizing light-induced damage to the specimen.
              We review core principles and recent advances in light-sheet
              microscopy, with a focus on concepts and implementations relevant
              for applications in developmental biology. We discuss how
              light-sheet microcopy has helped advance our understanding of
              developmental processes from single-molecule to whole-organism
              studies, assess the potential for synergies with other
              state-of-the-art technologies, and introduce methods for
              computational image and data analysis. Finally, we explore the
              future trajectory of light-sheet microscopy, discuss key efforts
              to disseminate new light-sheet technology, and identify exciting
              opportunities for further advances.",
  journal  = "Annu. Rev. Cell Dev. Biol.",
  volume   =  35,
  pages    = "655--681",
  month    =  oct,
  year     =  2019,
  keywords = "cellular dynamics; embryonic development; fluorescence
              microscopy; image processing; light-sheet microscopy; live
              imaging",
  language = "en"
}

@ARTICLE{Tosheva2020-wq,
  title    = "Between life and death: strategies to reduce phototoxicity in
              super-resolution microscopy",
  author   = "Tosheva, Kalina L and Yuan, Yue and Matos Pereira, Pedro and
              Culley, Si{\^a}n and Henriques, Ricardo",
  abstract = "Super-resolution microscopy (SRM) enables non-invasive,
              molecule-specific imaging of the internal structure and dynamics
              of cells with sub-diffraction limit spatial resolution. One of
              its major limitations is the requirement for high-intensity
              illumination, generating considerable cellular phototoxicity.
              This factor considerably limits the capacity for live-cell
              observations, particularly for extended periods of time. Here, we
              give an overview of new developments in hardware, software and
              probe chemistry aiming to reduce phototoxicity. Additionally, we
              discuss how the choice of biological model and sample environment
              impacts the capacity for live-cell observations.",
  journal  = "J. Phys. D Appl. Phys.",
  volume   =  53,
  number   =  16,
  pages    = "163001",
  month    =  apr,
  year     =  2020,
  keywords = "fluorescence; photodamage; phototoxicity; super-resolution
              microscopy",
  language = "en"
}

@BOOK{Fay2018-mn,
  title     = "A biologist's guide to statistical thinking and analysis",
  author    = "Fay, David S and Gerow, Ken",
  abstract  = "The proper understanding and use of statistical tools are
               essential to the scientific enterprise. This is true both at the
               level of designing one's own experiments as well as for
               critically evaluating studies carried out by others.
               Unfortunately, many researchers who are otherwise rigorous and
               thoughtful in their scientific approach lack sufficient
               knowledge of this field. This methods chapter is written with
               such individuals in mind. Although the majority of examples are
               drawn from the field of Caenorhabditis elegans biology, the
               concepts and practical applications are also relevant to those
               who work in the disciplines of molecular genetics and cell and
               developmental biology. Our intent has been to limit theoretical
               considerations to a necessary minimum and to use common examples
               as illustrations for statistical analysis. Our chapter includes
               a description of basic terms and central concepts and also
               contains in-depth discussions on the analysis of means,
               proportions, ratios, probabilities, and correlations. We also
               address issues related to sample size, normality, outliers, and
               non-parametric approaches.",
  publisher = "WormBook",
  year      =  2018
}

@ARTICLE{Stemmer2008-ua,
  title    = "Widefield fluorescence microscopy with extended resolution",
  author   = "Stemmer, Andreas and Beck, Markus and Fiolka, Reto",
  abstract = "Widefield fluorescence microscopy is seeing dramatic improvements
              in resolution, reaching today 100 nm in all three dimensions.
              This gain in resolution is achieved by dispensing with uniform
              K{\"o}hler illumination. Instead, non-uniform excitation light
              patterns with sinusoidal intensity variations in one, two, or
              three dimensions are applied combined with powerful image
              reconstruction techniques. Taking advantage of non-linear
              fluorophore response to the excitation field, the resolution can
              be further improved down to several 10 nm. In this review
              article, we describe the image formation in the microscope and
              computational reconstruction of the high-resolution dataset when
              exciting the specimen with a harmonic light pattern conveniently
              generated by interfering laser beams forming standing waves. We
              will also discuss extensions to total internal reflection
              microscopy, non-linear microscopy, and three-dimensional imaging.",
  journal  = "Histochem. Cell Biol.",
  volume   =  130,
  number   =  5,
  pages    = "807--817",
  month    =  nov,
  year     =  2008,
  language = "en"
}

@ARTICLE{Halchenko2021-nd,
  title     = "{DataLad}: distributed system for joint management of code,
               data, and their relationship",
  author    = "Halchenko, Yaroslav and Meyer, Kyle and Poldrack, Benjamin and
               Solanky, Debanjum and Wagner, Adina and Gors, Jason and
               MacFarlane, Dave and Pustina, Dorian and Sochat, Vanessa and
               Ghosh, Satrajit and M{\"o}nch, Christian and Markiewicz,
               Christopher and Waite, Laura and Shlyakhter, Ilya and de la
               Vega, Alejandro and Hayashi, Soichi and H{\"a}usler, Christian
               and Poline, Jean-Baptiste and Kadelka, Tobias and Skyt{\'e}n,
               Kusti and Jarecka, Dorota and Kennedy, David and Strauss, Ted
               and Cieslak, Matt and Vavra, Peter and Ioanas, Horea-Ioan and
               Schneider, Robin and Pfl{\"u}ger, Mika and Haxby, James and
               Eickhoff, Simon and Hanke, Michael",
  journal   = "J. Open Source Softw.",
  publisher = "The Open Journal",
  volume    =  6,
  number    =  63,
  pages     = "3262",
  month     =  jul,
  year      =  2021,
  copyright = "http://creativecommons.org/licenses/by/4.0/"
}

@ARTICLE{Sanderson2014-ea,
  title    = "Fluorescence microscopy",
  author   = "Sanderson, Michael J and Smith, Ian and Parker, Ian and Bootman,
              Martin D",
  abstract = "Fluorescence microscopy is a major tool with which to monitor
              cell physiology. Although the concepts of fluorescence and its
              optical separation using filters remain similar, microscope
              design varies with the aim of increasing image contrast and
              spatial resolution. The basics of wide-field microscopy are
              outlined to emphasize the selection, advantages, and correct use
              of laser scanning confocal microscopy, two-photon microscopy,
              scanning disk confocal microscopy, total internal reflection, and
              super-resolution microscopy. In addition, the principles of how
              these microscopes form images are reviewed to appreciate their
              capabilities, limitations, and constraints for operation.",
  journal  = "Cold Spring Harb. Protoc.",
  volume   =  2014,
  number   =  10,
  pages    = "db.top071795",
  month    =  oct,
  year     =  2014,
  language = "en"
}

@ARTICLE{Irgen-Gioro2022-gu,
  title    = "Fixation can change the appearance of phase separation in living
              cells",
  author   = "Irgen-Gioro, Shawn and Yoshida, Shawn and Walling, Victoria and
              Chong, Shasha",
  abstract = "Fixing cells with paraformaldehyde (PFA) is an essential step in
              numerous biological techniques as it is thought to preserve a
              snapshot of biomolecular transactions in living cells. Fixed-cell
              imaging techniques such as immunofluorescence have been widely
              used to detect liquid-liquid phase separation (LLPS) in vivo.
              Here, we compared images, before and after fixation, of cells
              expressing intrinsically disordered proteins that are able to
              undergo LLPS. Surprisingly, we found that PFA fixation can both
              enhance and diminish putative LLPS behaviors. For specific
              proteins, fixation can even cause their droplet-like puncta to
              artificially appear in cells that do not have any detectable
              puncta in the live condition. Fixing cells in the presence of
              glycine, a molecule that modulates fixation rates, can reverse
              the fixation effect from enhancing to diminishing LLPS
              appearance. We further established a kinetic model of fixation in
              the context of dynamic protein-protein interactions. Simulations
              based on the model suggest that protein localization in fixed
              cells depends on an intricate balance of protein-protein
              interaction dynamics, the overall rate of fixation, and notably,
              the difference between fixation rates of different proteins.
              Consistent with simulations, live-cell single-molecule imaging
              experiments showed that a fast overall rate of fixation relative
              to protein-protein interaction dynamics can minimize fixation
              artifacts. Our work reveals that PFA fixation changes the
              appearance of LLPS from living cells, presents a caveat in
              studying LLPS using fixation-based methods, and suggests a
              mechanism underlying the fixation artifact.",
  journal  = "Elife",
  volume   =  11,
  month    =  nov,
  year     =  2022,
  keywords = "cell biology; cross-linking; fixation; intrinsically disordered
              proteins; liquid--liquid phase separation; live-cell
              single-molecule imaging; multivalent protein--protein
              interactions; none; paraformaldehyde; physics of living systems",
  language = "en"
}

@ARTICLE{Neyman1928-mx,
  title     = "{ON} {THE} {USE} {AND} {INTERPRETATION} {OF} {CERTAIN} {TEST}
               {CRITERIA} {FOR} {PURPOSES} {OF} {STATISTICAL} {INFERENCE}",
  author    = "Neyman, J and Pearson, E S",
  abstract  = "J. NEYMAN, PH.D., E. S. PEARSON, D.Sc.; ON THE USE AND
               INTERPRETATION OF CERTAIN TEST CRITERIA FOR PURPOSES OF
               STATISTICAL INFERENCE, Biometrika, Volume 20A, Is",
  journal   = "Biometrika",
  publisher = "Oxford Academic",
  volume    = "20A",
  number    = "3-4",
  pages     = "263--294",
  month     =  dec,
  year      =  1928
}

@ARTICLE{Schneider2012-gs,
  title    = "{NIH} Image to {ImageJ}: 25 years of image analysis",
  author   = "Schneider, Caroline A and Rasband, Wayne S and Eliceiri, Kevin W",
  abstract = "For the past 25 years NIH Image and ImageJ software have been
              pioneers as open tools for the analysis of scientific images. We
              discuss the origins, challenges and solutions of these two
              programs, and how their history can serve to advise and inform
              other software projects.",
  journal  = "Nat. Methods",
  volume   =  9,
  number   =  7,
  pages    = "671--675",
  month    =  jul,
  year     =  2012,
  language = "en"
}

@MISC{Kenneth_R_Spring_Hiroshi_Komatsu_Martin_L_Scott_Stanley_A_Schwartz_Thomas_J_Fellers_Kathleen_E_Carr_Matthew_Parry-Hill_Michael_W_Davidson_undated-md,
  title        = "Basic Concepts and Formulas in Microscopy",
  booktitle    = "Nikon's {MicroscopyU}",
  author       = "{Kenneth R. Spring, Hiroshi Komatsu, Martin L. Scott ,
                  Stanley A. Schwartz, Thomas J. Fellers, Kathleen E. Carr,
                  Matthew Parry-Hill, Michael W. Davidson}",
  abstract     = "A thorough understanding of the concepts and formulas in
                  optical microscopy is essential in obtaining maximum
                  performance of the instrument.",
  howpublished = "\url{https://www.microscopyu.com/microscopy-basics}",
  note         = "Accessed: NA-NA-NA"
}

@ARTICLE{Bauch2011-wi,
  title    = "{openBIS}: a flexible framework for managing and analyzing
              complex data in biology research",
  author   = "Bauch, Angela and Adamczyk, Izabela and Buczek, Piotr and Elmer,
              Franz-Josef and Enimanev, Kaloyan and Glyzewski, Pawel and
              Kohler, Manuel and Pylak, Tomasz and Quandt, Andreas and
              Ramakrishnan, Chandrasekhar and Beisel, Christian and
              Malmstr{\"o}m, Lars and Aebersold, Ruedi and Rinn, Bernd",
  abstract = "BACKGROUND: Modern data generation techniques used in distributed
              systems biology research projects often create datasets of
              enormous size and diversity. We argue that in order to overcome
              the challenge of managing those large quantitative datasets and
              maximise the biological information extracted from them, a sound
              information system is required. Ease of integration with data
              analysis pipelines and other computational tools is a key
              requirement for it. RESULTS: We have developed openBIS, an open
              source software framework for constructing user-friendly,
              scalable and powerful information systems for data and metadata
              acquired in biological experiments. openBIS enables users to
              collect, integrate, share, publish data and to connect to data
              processing pipelines. This framework can be extended and has been
              customized for different data types acquired by a range of
              technologies. CONCLUSIONS: openBIS is currently being used by
              several SystemsX.ch and EU projects applying mass spectrometric
              measurements of metabolites and proteins, High Content Screening,
              or Next Generation Sequencing technologies. The attributes that
              make it interesting to a large research community involved in
              systems biology projects include versatility, simplicity in
              deployment, scalability to very large data, flexibility to handle
              any biological data type and extensibility to the needs of any
              research domain.",
  journal  = "BMC Bioinformatics",
  volume   =  12,
  pages    = "468",
  month    =  dec,
  year     =  2011,
  language = "en"
}

@ARTICLE{Krishnamurthy2019-fk,
  title    = "Ex Vivo Microscopy: A Promising {Next-Generation} Digital
              Microscopy Tool for Surgical Pathology Practice",
  author   = "Krishnamurthy, Savitri and Brown, Jonathan Quincy and Iftimia,
              Nicusor and Levenson, Richard M and Rajadhyaksha, Milind",
  abstract = "CONTEXT.---: The rapid evolution of optical imaging modalities in
              recent years has opened the opportunity for ex vivo tissue
              imaging, which has significant implications for surgical
              pathology practice. These modalities have promising potential to
              be used as next-generation digital microscopy tools for
              examination of fresh tissue, with or without labeling with
              contrast agents. OBJECTIVE.---: To review the literature
              regarding various types of ex vivo optical imaging platforms that
              can generate digital images for tissue recognition with potential
              for utilization in anatomic pathology clinical practices. DATA
              SOURCES.---: Literature relevant to ex vivo tissue imaging
              obtained from the PubMed database. CONCLUSIONS.---: Ex vivo
              imaging of tissues can be performed by using various types of
              optical imaging techniques. These next-generation digital
              microscopy tools have a promising potential for utilization in
              surgical pathology practice.",
  journal  = "Arch. Pathol. Lab. Med.",
  volume   =  143,
  number   =  9,
  pages    = "1058--1068",
  month    =  sep,
  year     =  2019,
  language = "en"
}

@ARTICLE{Rossner2004-be,
  title    = "What's in a picture? The temptation of image manipulation",
  author   = "Rossner, Mike and Yamada, Kenneth M",
  journal  = "J. Cell Biol.",
  volume   =  166,
  number   =  1,
  pages    = "11--15",
  month    =  jul,
  year     =  2004,
  language = "en"
}

@ARTICLE{Lord2020-sp,
  title    = "{SuperPlots}: Communicating reproducibility and variability in
              cell biology",
  author   = "Lord, Samuel J and Velle, Katrina B and Mullins, R Dyche and
              Fritz-Laylin, Lillian K",
  abstract = "P values and error bars help readers infer whether a reported
              difference would likely recur, with the sample size n used for
              statistical tests representing biological replicates, independent
              measurements of the population from separate experiments. We
              provide examples and practical tutorials for creating figures
              that communicate both the cell-level variability and the
              experimental reproducibility.",
  journal  = "J. Cell Biol.",
  volume   =  219,
  number   =  6,
  month    =  jun,
  year     =  2020,
  language = "en"
}

@ARTICLE{Uhlen2016-wy,
  title    = "A proposal for validation of antibodies",
  author   = "Uhlen, Mathias and Bandrowski, Anita and Carr, Steven and
              Edwards, Aled and Ellenberg, Jan and Lundberg, Emma and Rimm,
              David L and Rodriguez, Henry and Hiltke, Tara and Snyder, Michael
              and Yamamoto, Tadashi",
  journal  = "Nat. Methods",
  volume   =  13,
  number   =  10,
  pages    = "823--827",
  month    =  oct,
  year     =  2016,
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Shapiro1965-xf,
  title     = "An analysis of variance test for normality (complete samples)",
  author    = "Shapiro, S S and Wilk, M B",
  abstract  = "S. S. SHAPIRO, M. B. WILK; An analysis of variance test for
               normality (complete samples)†, Biometrika, Volume 52, Issue 3-4,
               1 December 1965, Pages 591--611, htt",
  journal   = "Biometrika",
  publisher = "Oxford Academic",
  volume    =  52,
  number    = "3-4",
  pages     = "591--611",
  month     =  dec,
  year      =  1965
}


@ARTICLE{Rueden2017-ku,
  title    = "{ImageJ2}: {ImageJ} for the next generation of scientific image
              data",
  author   = "Rueden, Curtis T and Schindelin, Johannes and Hiner, Mark C and
              DeZonia, Barry E and Walter, Alison E and Arena, Ellen T and
              Eliceiri, Kevin W",
  abstract = "BACKGROUND: ImageJ is an image analysis program extensively used
              in the biological sciences and beyond. Due to its ease of use,
              recordable macro language, and extensible plug-in architecture,
              ImageJ enjoys contributions from non-programmers, amateur
              programmers, and professional developers alike. Enabling such a
              diversity of contributors has resulted in a large community that
              spans the biological and physical sciences. However, a rapidly
              growing user base, diverging plugin suites, and technical
              limitations have revealed a clear need for a concerted software
              engineering effort to support emerging imaging paradigms, to
              ensure the software's ability to handle the requirements of
              modern science. RESULTS: We rewrote the entire ImageJ codebase,
              engineering a redesigned plugin mechanism intended to facilitate
              extensibility at every level, with the goal of creating a more
              powerful tool that continues to serve the existing community
              while addressing a wider range of scientific requirements. This
              next-generation ImageJ, called ``ImageJ2'' in places where the
              distinction matters, provides a host of new functionality. It
              separates concerns, fully decoupling the data model from the user
              interface. It emphasizes integration with external applications
              to maximize interoperability. Its robust new plugin framework
              allows everything from image formats, to scripting languages, to
              visualization to be extended by the community. The redesigned
              data model supports arbitrarily large, N-dimensional datasets,
              which are increasingly common in modern image acquisition.
              Despite the scope of these changes, backwards compatibility is
              maintained such that this new functionality can be seamlessly
              integrated with the classic ImageJ interface, allowing users and
              developers to migrate to these new methods at their own pace.
              CONCLUSIONS: Scientific imaging benefits from open-source
              programs that advance new method development and deployment to a
              diverse audience. ImageJ has continuously evolved with this idea
              in mind; however, new and emerging scientific requirements have
              posed corresponding challenges for ImageJ's development. The
              described improvements provide a framework engineered for
              flexibility, intended to support these requirements as well as
              accommodate future needs. Future efforts will focus on
              implementing new algorithms in this framework and expanding
              collaborations with other popular scientific software suites.",
  journal  = "BMC Bioinformatics",
  volume   =  18,
  number   =  1,
  pages    = "529",
  month    =  nov,
  year     =  2017,
  keywords = "Extensibility; Image processing; ImageJ; ImageJ2;
              Interoperability; N-dimensional; Open development; Open source;
              Reproducibility",
  language = "en"
}

@ARTICLE{Caicedo2017-ks,
  title    = "Data-analysis strategies for image-based cell profiling",
  author   = "Caicedo, Juan C and Cooper, Sam and Heigwer, Florian and Warchal,
              Scott and Qiu, Peng and Molnar, Csaba and Vasilevich, Aliaksei S
              and Barry, Joseph D and Bansal, Harmanjit Singh and Kraus, Oren
              and Wawer, Mathias and Paavolainen, Lassi and Herrmann, Markus D
              and Rohban, Mohammad and Hung, Jane and Hennig, Holger and
              Concannon, John and Smith, Ian and Clemons, Paul A and Singh,
              Shantanu and Rees, Paul and Horvath, Peter and Linington, Roger G
              and Carpenter, Anne E",
  abstract = "Image-based cell profiling is a high-throughput strategy for the
              quantification of phenotypic differences among a variety of cell
              populations. It paves the way to studying biological systems on a
              large scale by using chemical and genetic perturbations. The
              general workflow for this technology involves image acquisition
              with high-throughput microscopy systems and subsequent image
              processing and analysis. Here, we introduce the steps required to
              create high-quality image-based (i.e., morphological) profiles
              from a collection of microscopy images. We recommend techniques
              that have proven useful in each stage of the data analysis
              process, on the basis of the experience of 20 laboratories
              worldwide that are refining their image-based cell-profiling
              methodologies in pursuit of biological discovery. The recommended
              techniques cover alternatives that may suit various biological
              goals, experimental designs, and laboratories' preferences.",
  journal  = "Nat. Methods",
  volume   =  14,
  number   =  9,
  pages    = "849--863",
  month    =  aug,
  year     =  2017,
  language = "en"
}

@MISC{Baker2016-zn,
  title        = "1,500 scientists lift the lid on reproducibility",
  booktitle    = "Nature Publishing Group {UK}",
  author       = "Baker, Monya",
  abstract     = "Survey sheds light on the `crisis' rocking research.",
  month        =  may,
  year         =  2016,
  howpublished = "\url{http://dx.doi.org/10.1038/533452a}",
  note         = "Accessed: 2023-3-28",
  language     = "en"
}

@ARTICLE{Freedman2015-dx,
  title    = "The Economics of Reproducibility in Preclinical Research",
  author   = "Freedman, Leonard P and Cockburn, Iain M and Simcoe, Timothy S",
  abstract = "Low reproducibility rates within life science research undermine
              cumulative knowledge production and contribute to both delays and
              costs of therapeutic drug development. An analysis of past
              studies indicates that the cumulative (total) prevalence of
              irreproducible preclinical research exceeds 50\%, resulting in
              approximately US$28,000,000,000 (US$28B)/year spent on
              preclinical research that is not reproducible-in the United
              States alone. We outline a framework for solutions and a plan for
              long-term improvements in reproducibility rates that will help to
              accelerate the discovery of life-saving therapies and cures.",
  journal  = "PLoS Biol.",
  volume   =  13,
  number   =  6,
  pages    = "e1002165",
  month    =  jun,
  year     =  2015,
  language = "en"
}

@ARTICLE{Lambert2019-xl,
  title    = "{FPbase}: a community-editable fluorescent protein database",
  author   = "Lambert, Talley J",
  journal  = "Nat. Methods",
  volume   =  16,
  number   =  4,
  pages    = "277--278",
  month    =  apr,
  year     =  2019,
  language = "en"
}

@ARTICLE{Verdaasdonk2014-js,
  title    = "Bending the rules: widefield microscopy and the Abbe limit of
              resolution",
  author   = "Verdaasdonk, Jolien S and Stephens, Andrew D and Haase, Julian
              and Bloom, Kerry",
  abstract = "One of the most fundamental concepts of microscopy is that of
              resolution-the ability to clearly distinguish two objects as
              separate. Recent advances such as structured illumination
              microscopy (SIM) and point localization techniques including
              photoactivated localization microscopy (PALM), and stochastic
              optical reconstruction microscopy (STORM) strive to overcome the
              inherent limits of resolution of the modern light microscope.
              These techniques, however, are not always feasible or optimal for
              live cell imaging. Thus, in this review, we explore three
              techniques for extracting high resolution data from images
              acquired on a widefield microscope-deconvolution, model
              convolution, and Gaussian fitting. Deconvolution is a powerful
              tool for restoring a blurred image using knowledge of the point
              spread function (PSF) describing the blurring of light by the
              microscope, although care must be taken to ensure accuracy of
              subsequent quantitative analysis. The process of model
              convolution also requires knowledge of the PSF to blur a
              simulated image which can then be compared to the experimentally
              acquired data to reach conclusions regarding its geometry and
              fluorophore distribution. Gaussian fitting is the basis for point
              localization microscopy, and can also be applied to tracking spot
              motion over time or measuring spot shape and size. All together,
              these three methods serve as powerful tools for high-resolution
              imaging using widefield microscopy.",
  journal  = "J. Cell. Physiol.",
  volume   =  229,
  number   =  2,
  pages    = "132--138",
  month    =  feb,
  year     =  2014,
  language = "en"
}

@ARTICLE{Kolmogorov1933-yn,
  title    = "Sulla determinazione empirica di una lgge di distribuzione",
  author   = "Kolmogorov, A N",
  journal  = "G. Ist. Ital. Attuari.",
  volume   =  4,
  pages    = "83--91",
  year     =  1933
}

@ARTICLE{Swedlow2002-fe,
  title    = "Live cell imaging using wide-field microscopy and deconvolution",
  author   = "Swedlow, Jason R and Platani, Melpomeni",
  abstract = "The use of fluorescence imaging methods, most recently based on
              fluorescent protein technology, and the availability of high
              quality fluorescence imaging systems have driven a revolution in
              cell and molecular biology. Live cell imaging, especially using
              fluorescence, is now used in a wide variety of assays in academic
              and commercial laboratories. The use of this technology requires
              particular attention to be paid to cell engineering, the design
              of the image acquisition system, the imaging protocol, and
              subsequent processing and analytic methods. In this review, we
              discuss each of these steps, highlighting practical techniques
              developed by us and others.",
  journal  = "Cell Struct. Funct.",
  volume   =  27,
  number   =  5,
  pages    = "335--341",
  month    =  oct,
  year     =  2002,
  language = "en"
}

@ARTICLE{Wilkinson2016-ds,
  title    = "The {FAIR} Guiding Principles for scientific data management and
              stewardship",
  author   = "Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, I
              Jsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak,
              Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva
              Santos, Luiz Bonino and Bourne, Philip E and Bouwman, Jildau and
              Brookes, Anthony J and Clark, Tim and Crosas, Merc{\`e} and
              Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo,
              Chris T and Finkers, Richard and Gonzalez-Beltran, Alejandra and
              Gray, Alasdair J G and Groth, Paul and Goble, Carole and Grethe,
              Jeffrey S and Heringa, Jaap and 't Hoen, Peter A C and Hooft, Rob
              and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott
              J and Martone, Maryann E and Mons, Albert and Packer, Abel L and
              Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van
              Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and
              Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz,
              Morris A and Thompson, Mark and van der Lei, Johan and van
              Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and
              Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and
              Mons, Barend",
  abstract = "There is an urgent need to improve the infrastructure supporting
              the reuse of scholarly data. A diverse set of
              stakeholders-representing academia, industry, funding agencies,
              and scholarly publishers-have come together to design and jointly
              endorse a concise and measureable set of principles that we refer
              to as the FAIR Data Principles. The intent is that these may act
              as a guideline for those wishing to enhance the reusability of
              their data holdings. Distinct from peer initiatives that focus on
              the human scholar, the FAIR Principles put specific emphasis on
              enhancing the ability of machines to automatically find and use
              the data, in addition to supporting its reuse by individuals.
              This Comment is the first formal publication of the FAIR
              Principles, and includes the rationale behind them, and some
              exemplar implementations in the community.",
  journal  = "Sci Data",
  volume   =  3,
  pages    = "160018",
  month    =  mar,
  year     =  2016,
  language = "en"
}

@MISC{Sofroniew2022-nd,
  title  = "napari: a multi-dimensional image viewer for Python",
  author = "Sofroniew, Nicholas and Lambert, Talley and Evans, Kira and
            Nunez-Iglesias, Juan and Bokota, Grzegorz and Winston, Philip and
            Pe{\~n}a-Castellanos, Gonzalo and Yamauchi, Kevin and Bussonnier,
            Matthias and Doncila Pop, Draga and Can Solak, Ahmet and Liu,
            Ziyang and Wadhwa, Pam and Burt, Alister and Buckley, Genevieve and
            Sweet, Andrew and Migas, Lukasz and Hilsenstein, Volker and Gaifas,
            Lorenzo and Bragantini, Jord{\~a}o and Rodr{\'\i}guez-Guerra, Jaime
            and Mu{\~n}oz, Hector and Freeman, Jeremy and Boone, Peter and
            Lowe, Alan and Gohlke, Christoph and Royer, Loic and Pierr{\'e},
            Andrea and Har-Gil, Hagai and McGovern, Abigail",
  month  =  nov,
  year   =  2022
}

@BOOK{Chakravarti1967-jl,
  title    = "Handbook of Methods of Applied Statistics: Planning collection of
              data",
  publisher = "Wiley",
  author   = "Chakravarti, Indra Mohan and Laha, R G and Roy, Jogabrata",
  year     =  1967,
  language = "en"
}

@ARTICLE{Jambor2021-qe,
  title    = "Creating clear and informative image-based figures for scientific
              publications",
  author   = "Jambor, Helena and Antonietti, Alberto and Alicea, Bradly and
              Audisio, Tracy L and Auer, Susann and Bhardwaj, Vivek and
              Burgess, Steven J and Ferling, Iuliia and Gazda, Ma{\l}gorzata
              Anna and Hoeppner, Luke H and Ilangovan, Vinodh and Lo, Hung and
              Olson, Mischa and Mohamed, Salem Yousef and Sarabipour, Sarvenaz
              and Varma, Aalok and Walavalkar, Kaivalya and Wissink, Erin M and
              Weissgerber, Tracey L",
  abstract = "Scientists routinely use images to display data. Readers often
              examine figures first; therefore, it is important that figures
              are accessible to a broad audience. Many resources discuss
              fraudulent image manipulation and technical specifications for
              image acquisition; however, data on the legibility and
              interpretability of images are scarce. We systematically examined
              these factors in non-blot images published in the top 15 journals
              in 3 fields; plant sciences, cell biology, and physiology (n =
              580 papers). Common problems included missing scale bars,
              misplaced or poorly marked insets, images or labels that were not
              accessible to colorblind readers, and insufficient explanations
              of colors, labels, annotations, or the species and tissue or
              object depicted in the image. Papers that met all good practice
              criteria examined for all image-based figures were uncommon
              (physiology 16\%, cell biology 12\%, plant sciences 2\%). We
              present detailed descriptions and visual examples to help
              scientists avoid common pitfalls when publishing images. Our
              recommendations address image magnification, scale information,
              insets, annotation, and color and may encourage discussion about
              quality standards for bioimage publishing.",
  journal  = "PLoS Biol.",
  volume   =  19,
  number   =  3,
  pages    = "e3001161",
  month    =  mar,
  year     =  2021,
  language = "en"
}

@ARTICLE{Levet2021-as,
  title    = "Developing open-source software for bioimage analysis:
              opportunities and challenges",
  author   = "Levet, Florian and Carpenter, Anne E and Eliceiri, Kevin W and
              Kreshuk, Anna and Bankhead, Peter and Haase, Robert",
  abstract = "Fast-paced innovations in imaging have resulted in single systems
              producing exponential amounts of data to be analyzed.
              Computational methods developed in computer science labs have
              proven to be crucial for analyzing these data in an unbiased and
              efficient manner, reaching a prominent role in most microscopy
              studies. Still, their use usually requires expertise in bioimage
              analysis, and their accessibility for life scientists has
              therefore become a bottleneck. Open-source software for bioimage
              analysis has developed to disseminate these computational methods
              to a wider audience, and to life scientists in particular. In
              recent years, the influence of many open-source tools has grown
              tremendously, helping tens of thousands of life scientists in the
              process. As creators of successful open-source bioimage analysis
              software, we here discuss the motivations that can initiate
              development of a new tool, the common challenges faced, and the
              characteristics required for achieving success.",
  journal  = "F1000Res.",
  volume   =  10,
  pages    = "302",
  month    =  apr,
  year     =  2021,
  keywords = "Open-source; bioimage analysis; life science; software",
  language = "en"
}

@MISC{noauthor_undated-qv,
  title        = "Compatible Immersion Oil for Polymer Coverslip- {FAQ} - ibidi",
  abstract     = "When using oil immersion objectives with the ibidi Polymer
                  Coverslip, use only the immersion oils specified in the table
                  below.",
  howpublished = "\url{https://ibidi.com/content/551-immersion-oils-compatible-with-ibidi-labware-products}",
  note         = "Accessed: 2022-12-6",
  language     = "en"
}

@ARTICLE{Miura2021-mb,
  title    = "Reproducible image handling and analysis",
  author   = "Miura, Kota and N{\o}rrelykke, Simon F",
  abstract = "Image data are universal in life sciences research. Their proper
              handling is not. A significant proportion of image data in
              research papers show signs of mishandling that undermine their
              interpretation. We propose that a precise description of the
              image processing and analysis applied is required to address this
              problem. A new norm for reporting reproducible image analyses
              will diminish mishandling, as it will alert co-authors, referees,
              and journals to aberrant image data processing or, if published
              nonetheless, it will document it to the reader. To promote this
              norm, we discuss the effectiveness of this approach and give some
              step-by-step instructions for publishing reproducible image data
              processing and analysis workflows.",
  journal  = "EMBO J.",
  volume   =  40,
  number   =  3,
  pages    = "e105889",
  month    =  feb,
  year     =  2021,
  language = "en"
}

@ARTICLE{De_Chaumont2012-pe,
  title    = "Icy: an open bioimage informatics platform for extended
              reproducible research",
  author   = "de Chaumont, Fabrice and Dallongeville, St{\'e}phane and
              Chenouard, Nicolas and Herv{\'e}, Nicolas and Pop, Sorin and
              Provoost, Thomas and Meas-Yedid, Vannary and Pankajakshan,
              Praveen and Lecomte, Timoth{\'e}e and Le Montagner, Yoann and
              Lagache, Thibault and Dufour, Alexandre and Olivo-Marin,
              Jean-Christophe",
  abstract = "Current research in biology uses evermore complex computational
              and imaging tools. Here we describe Icy, a collaborative bioimage
              informatics platform that combines a community website for
              contributing and sharing tools and material, and software with a
              high-end visual programming framework for seamless development of
              sophisticated imaging workflows. Icy extends the reproducible
              research principles, by encouraging and facilitating the
              reusability, modularity, standardization and management of
              algorithms and protocols. Icy is free, open-source and available
              at http://icy.bioimageanalysis.org/.",
  journal  = "Nat. Methods",
  volume   =  9,
  number   =  7,
  pages    = "690--696",
  month    =  jun,
  year     =  2012,
  language = "en"
}

@ARTICLE{Galaxy_Community2022-uh,
  title    = "The Galaxy platform for accessible, reproducible and
              collaborative biomedical analyses: 2022 update",
  author   = "{Galaxy Community}",
  abstract = "Galaxy is a mature, browser accessible workbench for scientific
              computing. It enables scientists to share, analyze and visualize
              their own data, with minimal technical impediments. A thriving
              global community continues to use, maintain and contribute to the
              project, with support from multiple national infrastructure
              providers that enable freely accessible analysis and training
              services. The Galaxy Training Network supports free,
              self-directed, virtual training with >230 integrated tutorials.
              Project engagement metrics have continued to grow over the last 2
              years, including source code contributions, publications,
              software packages wrapped as tools, registered users and their
              daily analysis jobs, and new independent specialized servers. Key
              Galaxy technical developments include an improved user interface
              for launching large-scale analyses with many files, interactive
              tools for exploratory data analysis, and a complete suite of
              machine learning tools. Important scientific developments enabled
              by Galaxy include Vertebrate Genome Project (VGP) assembly
              workflows and global SARS-CoV-2 collaborations.",
  journal  = "Nucleic Acids Res.",
  volume   =  50,
  number   = "W1",
  pages    = "W345--51",
  month    =  apr,
  year     =  2022,
  language = "en"
}

@INCOLLECTION{Waters2014-aj,
  title     = "Chapter 1 - Concepts in quantitative fluorescence microscopy",
  booktitle = "Methods in Cell Biology",
  author    = "Waters, Jennifer C and Wittmann, Torsten",
  editor    = "Waters, Jennifer C and Wittman, Torsten",
  abstract  = "In recent years, there has been an enormous increase in the
               publication of spatial and temporal measurements made on
               fluorescence microscopy digital images. Quantitative
               fluorescence microscopy is a powerful and important tool in
               biological research but is also an error-prone technique that
               requires careful attention to detail. In this chapter, we focus
               on general concepts that are critical to performing accurate and
               precise quantitative fluorescence microscopy measurements.",
  publisher = "Academic Press",
  volume    =  123,
  pages     = "1--18",
  month     =  jan,
  year      =  2014,
  keywords  = "Quantitation; Quantitate; Quantify; Measure; Fluorescence;
               Intensity; Resolution; Sampling; Microscopy"
}

@ARTICLE{Rueden2019-qp,
  title     = "Scientific Community Image Forum: A discussion forum for
               scientific image software",
  author    = "Rueden, Curtis T and Ackerman, Jeanelle and Arena, Ellen T and
               Eglinger, Jan and Cimini, Beth A and Goodman, Allen and
               Carpenter, Anne E and Eliceiri, Kevin W",
  abstract  = "Forums and email lists play a major role in assisting scientists
               in using software. Previously, each open-source bioimaging
               software package had its own distinct forum or email list.
               Although each provided access to experts from various software
               teams, this fragmentation resulted in many scientists not
               knowing where to begin with their projects. Thus, the scientific
               imaging community lacked a central platform where solutions
               could be discussed in an open, software-independent manner. In
               response, we introduce the Scientific Community Image Forum,
               where users can pose software-related questions about digital
               image analysis, acquisition, and data management.",
  journal   = "PLoS Biol.",
  publisher = "Public Library of Science (PLoS)",
  volume    =  17,
  number    =  6,
  pages     = "e3000340",
  month     =  jun,
  year      =  2019,
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en"
}

@MISC{Frigault2009-af,
  title   = "Live-cell microscopy -- tips and tools",
  author  = "Frigault, Melanie M and Lacoste, Judith and Swift, Jody L and
             Brown, Claire M",
  journal = "Journal of Cell Science",
  volume  =  122,
  number  =  6,
  pages   = "753--767",
  year    =  2009
}

@MISC{Executable_Books_Community2020-op,
  title  = "Jupyter Book",
  author = "{Executable Books Community}",
  month  =  feb,
  year   =  2020
}

@MISC{Bankhead2022-xq,
  title        = "Introduction to Bioimage Analysis",
  author       = "Bankhead, Pete",
  year         =  2022,
  howpublished = "\url{https://bioimagebook.github.io/README.html}",
  note         = "Accessed: NA-NA-NA"
}

@ARTICLE{Cromey2010-jr,
  title    = "Avoiding twisted pixels: ethical guidelines for the appropriate
              use and manipulation of scientific digital images",
  author   = "Cromey, Douglas W",
  abstract = "Digital imaging has provided scientists with new opportunities to
              acquire and manipulate data using techniques that were difficult
              or impossible to employ in the past. Because digital images are
              easier to manipulate than film images, new problems have emerged.
              One growing concern in the scientific community is that digital
              images are not being handled with sufficient care. The problem is
              twofold: (1) the very small, yet troubling, number of intentional
              falsifications that have been identified, and (2) the more common
              unintentional, inappropriate manipulation of images for
              publication. Journals and professional societies have begun to
              address the issue with specific digital imaging guidelines.
              Unfortunately, the guidelines provided often do not come with
              instructions to explain their importance. Thus they deal with
              what should or should not be done, but not the associated 'why'
              that is required for understanding the rules. This article
              proposes 12 guidelines for scientific digital image manipulation
              and discusses the technical reasons behind these guidelines.
              These guidelines can be incorporated into lab meetings and
              graduate student training in order to provoke discussion and
              begin to bring an end to the culture of ``data beautification''.",
  journal  = "Sci. Eng. Ethics",
  volume   =  16,
  number   =  4,
  pages    = "639--667",
  month    =  dec,
  year     =  2010,
  language = "en"
}

@ARTICLE{Schmied2023-ad,
  title         = "Community-developed checklists for publishing images and
                   image analysis",
  author        = "Schmied, Christopher and Nelson, Michael and Avilov, Sergiy
                   and Bakker, Gert-Jan and Bertocchi, Cristina and Bischof,
                   Johanna and Boehm, Ulrike and Brocher, Jan and Carvalho,
                   Mariana and Chiritescu, Catalin and Christopher, Jana and
                   Cimini, Beth and Conde-Sousa, Eduardo and Ebner, Michael and
                   Ecker, Rupert and Eliceiri, Kevin and Fernandez-Rodriguez,
                   Julia and Gaudreault, Nathalie and Gelman, Laurent and
                   Grunwald, David and Gu, Tingting and Halidi, Nadia and
                   Hammer, Mathias and Hartley, Matthew and Held, Marie and
                   Jug, Florian and Kapoor, Varun and Koksoy, Ayse Aslihan and
                   Lacoste, Judith and Le D{\'e}v{\'e}dec, Sylvia and Le
                   Guyader, Sylvie and Liu, Penghuan and Martins, Gabriel and
                   Mathur, Aastha and Miura, Kota and Llopis, Paula Montero and
                   Nitschke, Roland and North, Alison and Parslow, Adam and
                   Payne-Dwyer, Alex and Plantard, Laure and Rizwan, Ali and
                   Schroth-Diez, Britta and Sch{\"u}tz, Lucas and Scott, Ryan T
                   and Seitz, Arne and Selchow, Olaf and Sharma, Ved and
                   Spitaler, Martin and Srinivasan, Sathya and De Castillia,
                   Caterina Strambio and Taatjes, Douglas and Tischer,
                   Christian and Jambor, Helena Klara",
  abstract      = "Images document scientific discoveries and are prevalent in
                   modern biomedical research. Microscopy imaging in particular
                   is currently undergoing rapid technological advancements.
                   However for scientists wishing to publish the obtained
                   images and image analyses results, there are to date no
                   unified guidelines. Consequently, microscopy images and
                   image data in publications may be unclear or difficult to
                   interpret. Here we present community-developed checklists
                   for preparing light microscopy images and image analysis for
                   publications. These checklists offer authors, readers, and
                   publishers key recommendations for image formatting and
                   annotation, color selection, data availability, and for
                   reporting image analysis workflows. The goal of our
                   guidelines is to increase the clarity and reproducibility of
                   image figures and thereby heighten the quality of microscopy
                   data is in publications.",
  month         =  feb,
  year          =  2023,
  archivePrefix = "arXiv",
  journal  = "arxiv",
  primaryClass  = "q-bio.OT",
  eprint        = "2302.07005"
}

@MISC{noauthor_2014-vq,
  title   = "Quantitative Imaging in Cell Biology",
  journal = "Methods in Cell Biology",
  year    =  2014
}

@ARTICLE{Jenny2007-hl,
  title    = "Color Design for the Color Vision Impaired",
  author   = "Jenny, Bernhard and Kelso, Nathaniel Vaughn",
  abstract = "Eight percent of men are affected by color vision impairment --
              they have difficulties distinguishing between colors and thus
              confuse certain colors that the majority of people see readily.
              Designers of maps and information graphics cannot disregard the
              needs of this relatively large group of media consumers. This
              article discusses the most common forms of color vision
              impairment, and introduces Color Oracle, a new software tool that
              assists the designer in verifying color schemes. Color Oracle
              filters maps and graphics in real-time and efficiently integrates
              with existing digital workflows. The paper also discusses color
              combinations and alternative visual variables for map symbology
              that those with color vision impairments can distinguish
              unambiguously. The presented techniques help the cartographer
              produce maps that are easy to read for those with color vision
              impairments and can still look good for those with normal color
              vision.",
  journal  = "CPJ",
  number   =  58,
  pages    = "61--67",
  month    =  sep,
  year     =  2007,
  language = "en"
}

@ARTICLE{Berg2019-no,
  title    = "ilastik: interactive machine learning for (bio)image analysis",
  author   = "Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and
              Straehle, Christoph N and Kausler, Bernhard X and Haubold,
              Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten
              and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I and Xu,
              Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong
              and Koethe, Ullrich and Hamprecht, Fred A and Kreshuk, Anna",
  abstract = "We present ilastik, an easy-to-use interactive tool that brings
              machine-learning-based (bio)image analysis to end users without
              substantial computational expertise. It contains pre-defined
              workflows for image segmentation, object classification, counting
              and tracking. Users adapt the workflows to the problem at hand by
              interactively providing sparse training annotations for a
              nonlinear classifier. ilastik can process data in up to five
              dimensions (3D, time and number of channels). Its computational
              back end runs operations on-demand wherever possible, allowing
              for interactive prediction on data larger than RAM. Once the
              classifiers are trained, ilastik workflows can be applied to new
              data from the command line without further user interaction. We
              describe all ilastik workflows in detail, including three case
              studies and a discussion on the expected performance.",
  journal  = "Nat. Methods",
  volume   =  16,
  number   =  12,
  pages    = "1226--1232",
  month    =  dec,
  year     =  2019,
  language = "en"
}

@ARTICLE{Richardson2021-rt,
  title    = "{TISSUE} {CLEARING}",
  author   = "Richardson, Douglas S and Guan, Webster and Matsumoto, Katsuhiko
              and Pan, Chenchen and Chung, Kwanghun and Ert{\"u}rk, Ali and
              Ueda, Hiroki R and Lichtman, Jeff W",
  abstract = "Tissue clearing of gross anatomical samples was first described
              over a century ago and has only recently found widespread use in
              the field of microscopy. This renaissance has been driven by the
              application of modern knowledge of optical physics and chemical
              engineering to the development of robust and reproducible
              clearing techniques, the arrival of new microscopes that can
              image large samples at cellular resolution and computing
              infrastructure able to store and analyze large data volumes. Many
              biological relationships between structure and function require
              investigation in three dimensions and tissue clearing therefore
              has the potential to enable broad discoveries in the biological
              sciences. Unfortunately, the current literature is complex and
              could confuse researchers looking to begin a clearing project.
              The goal of this Primer is to outline a modular approach to
              tissue clearing that allows a novice researcher to develop a
              customized clearing pipeline tailored to their tissue of
              interest. Further, the Primer outlines the required imaging and
              computational infrastructure needed to perform tissue clearing at
              scale, gives an overview of current applications, discusses
              limitations and provides an outlook on future advances in the
              field.",
  journal  = "Nat Rev Methods Primers",
  volume   =  1,
  number   =  1,
  month    =  dec,
  year     =  2021,
  language = "en"
}

@ARTICLE{Williams2017-yy,
  title    = "The Image Data Resource: A Bioimage Data Integration and
              Publication Platform",
  author   = "Williams, Eleanor and Moore, Josh and Li, Simon W and Rustici,
              Gabriella and Tarkowska, Aleksandra and Chessel, Anatole and Leo,
              Simone and Antal, B{\'a}lint and Ferguson, Richard K and Sarkans,
              Ugis and Brazma, Alvis and Salas, Rafael E Carazo and Swedlow,
              Jason R",
  abstract = "Access to primary research data is vital for the advancement of
              science. To extend the data types supported by community
              repositories, we built a prototype Image Data Resource (IDR) that
              collects and integrates imaging data acquired across many
              different imaging modalities. IDR links data from several imaging
              modalities, including high-content screening, super-resolution
              and time-lapse microscopy, digital pathology, public genetic or
              chemical databases, and cell and tissue phenotypes expressed
              using controlled ontologies. Using this integration, IDR
              facilitates the analysis of gene networks and reveals functional
              interactions that are inaccessible to individual studies. To
              enable re-analysis, we also established a computational resource
              based on Jupyter notebooks that allows remote access to the
              entire IDR. IDR is also an open source platform that others can
              use to publish their own image data. Thus IDR provides both a
              novel on-line resource and a software infrastructure that
              promotes and extends publication and re-analysis of scientific
              image data.",
  journal  = "Nat. Methods",
  volume   =  14,
  number   =  8,
  pages    = "775--781",
  month    =  aug,
  year     =  2017,
  language = "en"
}

@ARTICLE{Bogdanov2012-gh,
  title    = "Anti-fading media for live cell {GFP} imaging",
  author   = "Bogdanov, Alexey M and Kudryavtseva, Elena I and Lukyanov,
              Konstantin A",
  abstract = "Photostability is one of the most important characteristic of a
              dye for fluorescence microscopy. Recently we demonstrated that
              vitamins present in imaging media dramatically accelerate
              photobleaching of Enhanced Green Fluorescent Protein (EGFP) and
              many other green fluorescent and photoactivatable proteins. Here
              we tested all vitamins of commonly used media (such as Dulbecco's
              Modified Eagle Medium, DMEM) one-by-one and found that only two
              vitamins, riboflavin and pyridoxal, decrease photostability of
              EGFP. Thus, DMEM without riboflavin and pyridoxal can be used as
              an imaging medium, which ensures high photostability of GFPs at
              the expense of minimal biochemical disturbance. Then, we tested
              some antioxidants and found that a plant flavonoid rutin greatly
              enhances photostability of EGFP during live cell microscopy. In
              complete DMEM, rutin increased EGFP photostability up to the
              level of vitamin-depleted DMEM. Moreover, being added to
              vitamin-depleted DMEM, rutin was able to further suppress EGFP
              photobleaching. Potentially, new medium formulations can be
              widely used for fluorescence microscopy of GFP-expressing cells
              and model multicellular organisms in a variety of imaging
              applications, where photostability represents a challenge.",
  journal  = "PLoS One",
  volume   =  7,
  number   =  12,
  pages    = "e53004",
  month    =  dec,
  year     =  2012,
  language = "en"
}

@ARTICLE{Marques2020-nx,
  title    = "Imaging methods are vastly underreported in biomedical research",
  author   = "Marqu{\'e}s, Guillermo and Pengo, Thomas and Sanders, Mark A",
  abstract = "A variety of microscopy techniques are used by researchers in the
              life and biomedical sciences. As these techniques become more
              powerful and more complex, it is vital that scientific articles
              containing images obtained with advanced microscopes include full
              details about how each image was obtained. To explore the
              reporting of such details we examined 240 original research
              articles published in eight journals. We found that the quality
              of reporting was poor, with some articles containing no
              information about how images were obtained, and many articles
              lacking important basic details. Efforts by researchers, funding
              agencies, journals, equipment manufacturers and staff at shared
              imaging facilities are required to improve the reporting of
              experiments that rely on microscopy techniques.",
  journal  = "Elife",
  volume   =  9,
  month    =  aug,
  year     =  2020,
  keywords = "cell biology; developmental biology; imaging; microscopy; none;
              reporting standards; reproducibility; science forum",
  language = "en"
}

@MISC{Carpenter2006-ce,
  title  = "Genome Biology",
  author = "Carpenter, Anne E and Jones, Thouis R and Lamprecht, Michael R and
            Clarke, Colin and Kang, In and Friman, Ola and Guertin, David A and
            Chang, Joo and Lindquist, Robert A and Moffat, Jason and Golland,
            Polina and Sabatini, David M",
  volume =  7,
  number =  10,
  pages  = "R100",
  year   =  2006
}

@INCOLLECTION{Swedlow2007-ov,
  title     = "Quantitative Fluorescence Microscopy and Image Deconvolution",
  booktitle = "Methods in Cell Biology",
  author    = "Swedlow, Jason R",
  abstract  = "Publisher Summary Quantitative imaging and image de-convolution
               have become standard techniques for the modern cell biologist
               because they can form the basis of an increasing number of
               assays for molecular function in a cellular context. There are
               two major types of de-convolution approaches---deblurring and
               restoration algorithms. Deblurring algorithms remove blur but
               treat a series of optical sections as individual two-dimensional
               entities and therefore sometimes mishandle blurred light.
               Restoration algorithms determine an object that, when convolved
               with the point-spread function of the microscope, could produce
               the image data. The advantages and disadvantages of these
               methods are discussed in this chapter. Image de-convolution in
               fluorescence microscopy has usually been applied to
               high-resolution imaging to improve contrast and thus detect
               small, dim objects that might otherwise be obscured. Their
               proper use demands some consideration of the imaging hardware,
               the acquisition process, fundamental aspects of photon
               detection, and image processing. This can prove daunting for
               some cell biologists, but the power of these techniques has been
               proven many times in the works cited in the chapter and
               elsewhere. Their usage is now well defined, so they can be
               incorporated into the capabilities of most laboratories.",
  publisher = "Academic Press",
  volume    =  81,
  pages     = "447--465",
  month     =  jan,
  year      =  2007
}

@MISC{Holmes2019-no,
  title        = "Modern Statistics for Modern Biology",
  author       = "Holmes, Susan and Huber, Wolfgang",
  abstract     = "If you are a biologist and want to get the best out of the
                  powerful methods of modern computational statistics, this is
                  your book. You can visualize and analyze your own data, apply
                  unsupervised and supervised learning, integrate datasets,
                  apply hypothesis testing, and make publication-quality
                  figures using the power of R/Bioconductor and ggplot2. This
                  book will teach you 'cooking from scratch', from raw data to
                  beautiful illuminating output, as you learn to write your own
                  scripts in the R language and to use advanced statistics
                  packages from CRAN and Bioconductor. It covers a broad range
                  of basic and advanced topics important in the analysis of
                  high-throughput biological data, including principal
                  component analysis and multidimensional scaling, clustering,
                  multiple testing, unsupervised and supervised learning,
                  resampling, the pitfalls of experimental design, and power
                  simulations using Monte Carlo, and it even reaches networks,
                  trees, spatial statistics, image data, and microbial ecology.
                  Using a minimum of mathematical notation, it builds
                  understanding from well-chosen examples, simulation,
                  visualization, and above all hands-on interaction with data
                  and code.",
  month        =  feb,
  year         =  2019,
  howpublished = "\url{https://www.huber.embl.de/msmb/}",
  note         = "Accessed: NA-NA-NA"
}

@ARTICLE{Ustione2011-lp,
  title    = "A simple introduction to multiphoton microscopy",
  author   = "Ustione, A and Piston, D W",
  abstract = "Multiphoton microscopy is a powerful technique based on complex
              quantum mechanical effects. Thanks to the development of turnkey
              mode-locked laser systems, multiphoton microscopy is now
              available for everyone to use without extreme complexity. In this
              short introduction, we describe qualitatively the important
              concepts underlying the most commonly used type of multiphoton
              microscopy (two-photon excitation). We elucidate how those
              properties lead to the powerful results that have been achieved
              using this technique. As with any technique, two-photon
              excitation microscopy has limitations that we describe, and we
              provide examples of particular classes of experiments where
              two-photon excitation microscopy is advantageous over other
              approaches. Finally, we briefly describe other useful multiphoton
              microscopy approaches, such as three-photon excitation and second
              harmonic generation imaging.",
  journal  = "J. Microsc.",
  volume   =  243,
  number   =  3,
  pages    = "221--226",
  month    =  sep,
  year     =  2011,
  language = "en"
}

@ARTICLE{Bankhead2017-kz,
  title    = "{QuPath}: Open source software for digital pathology image
              analysis",
  author   = "Bankhead, Peter and Loughrey, Maurice B and Fern{\'a}ndez,
              Jos{\'e} A and Dombrowski, Yvonne and McArt, Darragh G and Dunne,
              Philip D and McQuaid, Stephen and Gray, Ronan T and Murray, Liam
              J and Coleman, Helen G and James, Jacqueline A and Salto-Tellez,
              Manuel and Hamilton, Peter W",
  abstract = "QuPath is new bioimage analysis software designed to meet the
              growing need for a user-friendly, extensible, open-source
              solution for digital pathology and whole slide image analysis. In
              addition to offering a comprehensive panel of tumor
              identification and high-throughput biomarker evaluation tools,
              QuPath provides researchers with powerful batch-processing and
              scripting functionality, and an extensible platform with which to
              develop and share new algorithms to analyze complex tissue
              images. Furthermore, QuPath's flexible design makes it suitable
              for a wide range of additional image analysis applications across
              biomedical research.",
  journal  = "Sci. Rep.",
  volume   =  7,
  number   =  1,
  pages    = "16878",
  month    =  dec,
  year     =  2017,
  language = "en"
}

@ARTICLE{Lucas2021-iq,
  title    = "Open-source deep-learning software for bioimage segmentation",
  author   = "Lucas, Alice M and Ryder, Pearl V and Li, Bin and Cimini, Beth A
              and Eliceiri, Kevin W and Carpenter, Anne E",
  abstract = "Microscopy images are rich in information about the dynamic
              relationships among biological structures. However, extracting
              this complex information can be challenging, especially when
              biological structures are closely packed, distinguished by
              texture rather than intensity, and/or low intensity relative to
              the background. By learning from large amounts of annotated data,
              deep learning can accomplish several previously intractable
              bioimage analysis tasks. Until the past few years, however, most
              deep-learning workflows required significant computational
              expertise to be applied. Here, we survey several new open-source
              software tools that aim to make deep-learning-based image
              segmentation accessible to biologists with limited computational
              experience. These tools take many different forms, such as web
              apps, plug-ins for existing imaging analysis software, and
              preconfigured interactive notebooks and pipelines. In addition to
              surveying these tools, we overview several challenges that remain
              in the field. We hope to expand awareness of the powerful
              deep-learning tools available to biologists for image analysis.",
  journal  = "Mol. Biol. Cell",
  volume   =  32,
  number   =  9,
  pages    = "823--829",
  month    =  apr,
  year     =  2021,
  language = "en"
}

@ARTICLE{Allan2012-rd,
  title    = "{OMERO}: flexible, model-driven data management for experimental
              biology",
  author   = "Allan, Chris and Burel, Jean-Marie and Moore, Josh and Blackburn,
              Colin and Linkert, Melissa and Loynton, Scott and Macdonald,
              Donald and Moore, William J and Neves, Carlos and Patterson,
              Andrew and Porter, Michael and Tarkowska, Aleksandra and
              Loranger, Brian and Avondo, Jerome and Lagerstedt, Ingvar and
              Lianas, Luca and Leo, Simone and Hands, Katherine and Hay, Ron T
              and Patwardhan, Ardan and Best, Christoph and Kleywegt, Gerard J
              and Zanetti, Gianluigi and Swedlow, Jason R",
  abstract = "Data-intensive research depends on tools that manage
              multidimensional, heterogeneous datasets. We built OME Remote
              Objects (OMERO), a software platform that enables access to and
              use of a wide range of biological data. OMERO uses a server-based
              middleware application to provide a unified interface for images,
              matrices and tables. OMERO's design and flexibility have enabled
              its use for light-microscopy, high-content-screening,
              electron-microscopy and even non-image-genotype data. OMERO is
              open-source software, available at http://openmicroscopy.org/.",
  journal  = "Nat. Methods",
  volume   =  9,
  number   =  3,
  pages    = "245--253",
  month    =  feb,
  year     =  2012,
  language = "en"
}

@TECHREPORT{Scholz2010-pm,
  title    = "Cell Adhesion and Growth on Coated or Modified Glass or Plastic
              Surfaces",
  author   = "Scholz, W K",
  institution = "Thermo Fisher Scientific",
  abstract = "Primary brain neurons did not adhere to unmodified plastic or
              glass surfaces, however, neurons adhered and differentiated on
              polylysine coated glass or plastic surfaces and, albeit
              differently, on both of the chemically modified glass surfaces.
              Growth substrates affect the adhesion, growth, morphology and
              differentiation of various cell types. However, the response to a
              given surface is cell type specific. The growth of various cell
              types on plastic, soda lime glass and borosilicate coverglass was
              examined. The surfaces were either unmodified, coated with
              polylysine, or stably surface modified with non-biological
              reagents or electrical discharge. Glass surfaces were chemically
              modified in two ways: using a proprietary procedure and reagents
              or as described by Kleinfeld, et al. (1988). All substrates were
              assembled into Thermo Scientific Nunc Lab-Tek and Lab-Tek II
              Chamber Slide products. Cells of the fibroblast-like cell line
              BHK-21 (baby hamster kidney), which are very adherent, did not
              distinguish between these growth surfaces. The less adherent
              fibroblast-like cell line L929 (mouse lung) and two
              epithelial-like cell lines, HEp-2 (human epidermal) and WISH
              (human amnion), grew to slightly higher densities and produced
              more uniform monolayers when grown on electric ally modified
              plastic compared to unmodified plastic or glass. These cell types
              also grew very well on both types of chemically modified glass
              surfaces. Primary brain neurons did not adhere to unmodified
              plastic or glass surfaces. However, neurons adhered and
              differentiated on polylysine coated glass or plastic surfaces
              and, albeit differently, on both of the chemically Technical
              Bulletin No. 13",
  year     =  2010,
  language = "en"
}

@ARTICLE{Haase2022-ad,
  title    = "A Hitchhiker's guide through the bio-image analysis software
              universe",
  author   = "Haase, Robert and Fazeli, Elnaz and Legland, David and Doube,
              Michael and Culley, Si{\^a}n and Belevich, Ilya and Jokitalo,
              Eija and Schorb, Martin and Klemm, Anna and Tischer, Christian",
  abstract = "Modern research in the life sciences is unthinkable without
              computational methods for extracting, quantifying and visualising
              information derived from microscopy imaging data of biological
              samples. In the past decade, we observed a dramatic increase in
              available software packages for these purposes. As it is
              increasingly difficult to keep track of the number of available
              image analysis platforms, tool collections, components and
              emerging technologies, we provide a conservative overview of
              software that we use in daily routine and give insights into
              emerging new tools. We give guidance on which aspects to consider
              when choosing the platform that best suits the user's needs,
              including aspects such as image data type, skills of the team,
              infrastructure and community at the institute and availability of
              time and budget.",
  journal  = "FEBS Lett.",
  volume   =  596,
  number   =  19,
  pages    = "2472--2485",
  month    =  oct,
  year     =  2022,
  keywords = "bio-image analysis; open-source; software",
  language = "en"
}

@ARTICLE{Jost2019-nx,
  title    = "Designing a rigorous microscopy experiment: Validating methods
              and avoiding bias",
  author   = "Jost, Anna Payne-Tobin and Waters, Jennifer C",
  abstract = "Images generated by a microscope are never a perfect
              representation of the biological specimen. Microscopes and
              specimen preparation methods are prone to error and can impart
              images with unintended attributes that might be misconstrued as
              belonging to the biological specimen. In addition, our brains are
              wired to quickly interpret what we see, and with an unconscious
              bias toward that which makes the most sense to us based on our
              current understanding. Unaddressed errors in microscopy images
              combined with the bias we bring to visual interpretation of
              images can lead to false conclusions and irreproducible imaging
              data. Here we review important aspects of designing a rigorous
              light microscopy experiment: validation of methods used to
              prepare samples and of imaging system performance, identification
              and correction of errors, and strategies for avoiding bias in the
              acquisition and analysis of images.",
  journal  = "J. Cell Biol.",
  volume   =  218,
  number   =  5,
  pages    = "1452--1466",
  month    =  may,
  year     =  2019,
  language = "en"
}

@ARTICLE{Montero_Llopis2021-nb,
  title    = "Best practices and tools for reporting reproducible fluorescence
              microscopy methods",
  author   = "Montero Llopis, Paula and Senft, Rebecca A and Ross-Elliott, Tim
              J and Stephansky, Ryan and Keeley, Daniel P and Koshar, Preman
              and Marqu{\'e}s, Guillermo and Gao, Ya-Sheng and Carlson,
              Benjamin R and Pengo, Thomas and Sanders, Mark A and Cameron,
              Lisa A and Itano, Michelle S",
  abstract = "Although fluorescence microscopy is ubiquitous in biomedical
              research, microscopy methods reporting is inconsistent and
              perhaps undervalued. We emphasize the importance of appropriate
              microscopy methods reporting and seek to educate researchers
              about how microscopy metadata impact data interpretation. We
              provide comprehensive guidelines and resources to enable accurate
              reporting for the most common fluorescence light microscopy
              modalities. We aim to improve microscopy reporting, thus
              improving the quality, rigor and reproducibility of image-based
              science.",
  journal  = "Nat. Methods",
  volume   =  18,
  number   =  12,
  pages    = "1463--1476",
  month    =  dec,
  year     =  2021,
  language = "en"
}

@ARTICLE{Waters2009-si,
  title    = "Accuracy and precision in quantitative fluorescence microscopy",
  author   = "Waters, Jennifer C",
  abstract = "The light microscope has long been used to document the
              localization of fluorescent molecules in cell biology research.
              With advances in digital cameras and the discovery and
              development of genetically encoded fluorophores, there has been a
              huge increase in the use of fluorescence microscopy to quantify
              spatial and temporal measurements of fluorescent molecules in
              biological specimens. Whether simply comparing the relative
              intensities of two fluorescent specimens, or using advanced
              techniques like F{\"o}rster resonance energy transfer (FRET) or
              fluorescence recovery after photobleaching (FRAP), quantitation
              of fluorescence requires a thorough understanding of the
              limitations of and proper use of the different components of the
              imaging system. Here, I focus on the parameters of digital image
              acquisition that affect the accuracy and precision of
              quantitative fluorescence microscopy measurements.",
  journal  = "J. Cell Biol.",
  volume   =  185,
  number   =  7,
  pages    = "1135--1148",
  month    =  jun,
  year     =  2009,
  language = "en"
}

@ARTICLE{Cancer_Genome_Atlas_Research_Network2013-mi,
  title    = "The Cancer Genome Atlas {Pan-Cancer} analysis project",
  author   = "{Cancer Genome Atlas Research Network} and Weinstein, John N and
              Collisson, Eric A and Mills, Gordon B and Shaw, Kenna R Mills and
              Ozenberger, Brad A and Ellrott, Kyle and Shmulevich, Ilya and
              Sander, Chris and Stuart, Joshua M",
  abstract = "The Cancer Genome Atlas (TCGA) Research Network has profiled and
              analyzed large numbers of human tumors to discover molecular
              aberrations at the DNA, RNA, protein and epigenetic levels. The
              resulting rich data provide a major opportunity to develop an
              integrated picture of commonalities, differences and emergent
              themes across tumor lineages. The Pan-Cancer initiative compares
              the first 12 tumor types profiled by TCGA. Analysis of the
              molecular aberrations and their functional roles across tumor
              types will teach us how to extend therapies effective in one
              cancer type to others with a similar genomic profile.",
  journal  = "Nat. Genet.",
  volume   =  45,
  number   =  10,
  pages    = "1113--1120",
  month    =  oct,
  year     =  2013,
  language = "en"
}
@MISC{Senft2023-cb,
  title  = "A biologist's guide to the field of quantitative bioimaging",
  author = "Senft, Rebecca A and Diaz-Rohrer, Barbara and Colarusso, Pina and
            Swift, Lucy and Jamali, Nasim and Jambor, Helena and Pengo, Thomas
            and Brideau, Craig and Llopis, Paula Montero and Uhlmann, Virginie
            and Kirk, Jason and Gonzales, Kevin Andrew and Bankhead, Peter and
            Evans, Iii, Edward L and Eliceiri, Kevin W and Cimini, Beth A",
  month  =  apr,
  year   =  2023
}

@ARTICLE{Ljosa2012-fr,
  title    = "Annotated high-throughput microscopy image sets for validation",
  author   = "Ljosa, Vebjorn and Sokolnicki, Katherine L and Carpenter, Anne E",
  journal  = "Nat. Methods",
  volume   =  9,
  number   =  7,
  pages    = "637",
  month    =  jun,
  year     =  2012,
  language = "en"
}

@MISC{noauthor_2022-bj,
  title        = "Training \& Education Resources",
  booktitle    = "{BioImaging} North America Site",
  abstract     = "Training and Education resources for microscopists seeking
                  online materials.",
  month        =  jan,
  year         =  2022,
  howpublished = "\url{https://www.bioimagingnorthamerica.org/training-education-resources/}",
  note         = "Accessed: 2023-4-10",
  language     = "en"
}

@ARTICLE{Paul-Gilloteaux2021-vw,
  title    = "Bioimage analysis workflows: community resources to navigate
              through a complex ecosystem",
  author   = "Paul-Gilloteaux, Perrine and Tosi, S{\'e}bastien and
              H{\'e}rich{\'e}, Jean-Karim and Gaignard, Alban and M{\'e}nager,
              Herv{\'e} and Mar{\'e}e, Rapha{\"e}l and Baecker, Volker and
              Klemm, Anna and Kala{\v s}, Mat{\'u}{\v s} and Zhang, Chong and
              Miura, Kota and Colombelli, Julien",
  abstract = "Workflows are the keystone of bioimage analysis, and the NEUBIAS
              (Network of European BioImage AnalystS) community is trying to
              gather the actors of this field and organize the information
              around them. One of its most recent outputs is the opening of the
              F1000Research NEUBIAS gateway, whose main objective is to offer a
              channel of publication for bioimage analysis workflows and
              associated resources. In this paper we want to express some
              personal opinions and recommendations related to finding,
              handling and developing bioimage analysis workflows. The
              emergence of ``big data'' in bioimaging and resource-intensive
              analysis algorithms make local data storage and computing
              solutions a limiting factor. At the same time, the need for data
              sharing with collaborators and a general shift towards remote
              work, have created new challenges and avenues for the execution
              and sharing of bioimage analysis workflows. These challenges are
              to reproducibly run workflows in remote environments, in
              particular when their components come from different software
              packages, but also to document them and link their parameters and
              results by following the FAIR principles (Findable, Accessible,
              Interoperable, Reusable) to foster open and reproducible science.
              In this opinion paper, we focus on giving some directions to the
              reader to tackle these challenges and navigate through this
              complex ecosystem, in order to find and use workflows, and to
              compare workflows addressing the same problem. We also discuss
              tools to run workflows in the cloud and on High Performance
              Computing resources, and suggest ways to make these workflows
              FAIR.",
  journal  = "F1000Res.",
  volume   =  10,
  pages    = "320",
  month    =  apr,
  year     =  2021,
  keywords = "Bioimage analysis; FAIR principles; NEUBIAS; collections;
              components; knowledge database; remote computing; scientific
              workflow management systems; workflows",
  language = "en"
}

@ARTICLE{Zhao2022-rm,
  title     = "Organoids",
  author    = "Zhao, Zixuan and Chen, Xinyi and Dowbaj, Anna M and Sljukic,
               Aleksandra and Bratlie, Kaitlin and Lin, Luda and Fong, Eliza Li
               Shan and Balachander, Gowri Manohari and Chen, Zhaowei and
               Soragni, Alice and Huch, Meritxell and Zeng, Yi Arial and Wang,
               Qun and Yu, Hanry",
  abstract  = "Organoids are simple tissue-engineered cell-based in vitro
               models that recapitulate many aspects of the complex structure
               and function of the corresponding in vivo tissue. They can be
               dissected and interrogated for fundamental mechanistic studies
               on development, regeneration and repair in human tissues, and
               can also be used in diagnostics, disease modelling, drug
               discovery and personalized medicine. Organoids are derived from
               either pluripotent or tissue-resident stem (embryonic or adult)
               or progenitor or differentiated cells from healthy or diseased
               tissues, such as tumours. To date, numerous organoid engineering
               strategies that support organoid culture and growth,
               proliferation, differentiation and maturation have been
               reported. This Primer highlights the rationale underlying the
               selection and development of these materials and methods to
               control the cellular/tissue niche; and therefore, the structure
               and function of the engineered organoid. We also discuss key
               considerations for generating robust organoids, such as those
               related to cell isolation and seeding, matrix and soluble factor
               selection, physical cues and integration. The general standards
               for data quality, reproducibility and deposition within the
               organoid community are also outlined. Lastly, we conclude by
               elaborating on the limitations of organoids in different
               applications, and the key priorities in organoid engineering for
               the coming years. Organoids are cell-based in vitro models
               derived from stem cells, reconstituting the complex structure
               and function of the corresponding tissue. In this Primer, Zhao,
               Chen, Dowbaj, Sljukic, Bratlie, Lin et al. discuss the
               development of organoids and methods for controlling their
               cellular environment.",
  journal   = "Nature Reviews Methods Primers",
  publisher = "Nature Publishing Group",
  volume    =  2,
  number    =  1,
  pages     = "1--21",
  month     =  dec,
  year      =  2022,
  language  = "en"
}

@ARTICLE{Jonkman2020-bo,
  title    = "Tutorial: guidance for quantitative confocal microscopy",
  author   = "Jonkman, James and Brown, Claire M and Wright, Graham D and
              Anderson, Kurt I and North, Alison J",
  abstract = "When used appropriately, a confocal fluorescence microscope is an
              excellent tool for making quantitative measurements in cells and
              tissues. The confocal microscope's ability to block out-of-focus
              light and thereby perform optical sectioning through a specimen
              allows the researcher to quantify fluorescence with very high
              spatial precision. However, generating meaningful data using
              confocal microscopy requires careful planning and a thorough
              understanding of the technique. In this tutorial, the researcher
              is guided through all aspects of acquiring quantitative confocal
              microscopy images, including optimizing sample preparation for
              fixed and live cells, choosing the most suitable microscope for a
              given application and configuring the microscope parameters.
              Suggestions are offered for planning unbiased and rigorous
              confocal microscope experiments. Common pitfalls such as
              photobleaching and cross-talk are addressed, as well as several
              troubling instrumentation problems that may prevent the
              acquisition of quantitative data. Finally, guidelines for
              analyzing and presenting confocal images in a way that maintains
              the quantitative nature of the data are presented, and
              statistical analysis is discussed. A visual summary of this
              tutorial is available as a poster
              (https://doi.org/10.1038/s41596-020-0307-7).",
  journal  = "Nat. Protoc.",
  volume   =  15,
  number   =  5,
  pages    = "1585--1611",
  month    =  may,
  year     =  2020,
  language = "en"
}

@ARTICLE{Wait2020-gq,
  title    = "Hypothesis-driven quantitative fluorescence microscopy - the
              importance of reverse-thinking in experimental design",
  author   = "Wait, Eric C and Reiche, Michael A and Chew, Teng-Leong",
  abstract = "One of the challenges in modern fluorescence microscopy is to
              reconcile the conventional utilization of microscopes as
              exploratory instruments with their emerging and rapidly expanding
              role as a quantitative tools. The contribution of microscopy to
              observational biology will remain enormous owing to the
              improvements in acquisition speed, imaging depth, resolution and
              biocompatibility of modern imaging instruments. However, the use
              of fluorescence microscopy to facilitate the quantitative
              measurements necessary to challenge hypotheses is a relatively
              recent concept, made possible by advanced optics, functional
              imaging probes and rapidly increasing computational power. We
              argue here that to fully leverage the rapidly evolving
              application of microscopes in hypothesis-driven biology, we not
              only need to ensure that images are acquired quantitatively but
              must also re-evaluate how microscopy-based experiments are
              designed. In this Opinion, we present a reverse logic that guides
              the design of quantitative fluorescence microscopy experiments.
              This unique approach starts from identifying the results that
              would quantitatively inform the hypothesis and map the process
              backward to microscope selection. This ensures that the
              quantitative aspects of testing the hypothesis remain the central
              focus of the entire experimental design.",
  journal  = "J. Cell Sci.",
  volume   =  133,
  number   =  21,
  month    =  nov,
  year     =  2020,
  keywords = "Experimental design; Hypothesis; Image analysis; Microscope
              choice; Microscopy; Quantitative analysis",
  language = "en"
}

@ARTICLE{Sanderson2020-qz,
  title    = "Fundamentals of Microscopy",
  author   = "Sanderson, Jeremy",
  abstract = "The light (or optical) microscope is the icon of science. The
              aphorism ``seeing is believing'' is often quoted in scientific
              papers involving microscopy. Unlike many scientific instruments,
              the light microscope will deliver an image however badly it is
              set up. Fluorescence microscopy is a widely used research tool
              across all disciplines of biological and biomedical science. Most
              universities and research institutions have microscopes,
              including confocal microscopes. This introductory paper in a
              series detailing advanced light microscopy techniques explains
              the foundations of both electron and light microscopy for
              biologists and life scientists working with the mouse. An
              explanation is given of how an image is formed. A description is
              given of how to set up a light microscope, whether it be a
              brightfield light microscope on the laboratory bench, a widefield
              fluorescence microscope, or a confocal microscope. These
              explanations are accompanied by operational protocols. A full
              explanation on how to set up and adjust a microscope according to
              the principles of K{\"o}hler illumination is given. The
              importance of Nyquist sampling is discussed. Guidelines are given
              on how to choose the best microscope to image the particular
              sample or slide preparation that you are working with. These are
              the basic principles of microscopy that a researcher must have an
              understanding of when operating core bioimaging facility
              instruments, in order to collect high-quality images.
              \copyright{} 2020 The Authors. Basic Protocol 1: Setting up
              K{\"o}hler illumination for a brightfield microscope Basic
              Protocol 2: Aligning the fluorescence bulb and setting up
              K{\"o}hler illumination for a widefield fluorescence microscope
              Basic Protocol 3: Generic protocol for operating a confocal
              microscope.",
  journal  = "Curr. Protoc. Mouse Biol.",
  volume   =  10,
  number   =  2,
  pages    = "e76",
  month    =  jun,
  year     =  2020,
  keywords = "K{\"o}hler illumination; confocal microscopy; fluorescence
              microscopy; image formation; light microscopy",
  language = "en"
}

@ARTICLE{Narla2018-qh,
  title    = "Automated Classification of Skin Lesions: From Pixels to Practice",
  author   = "Narla, Akhila and Kuprel, Brett and Sarin, Kavita and Novoa,
              Roberto and Ko, Justin",
  abstract = "The letters ``Interpretation of the Outputs of Deep Learning
              Model trained with Skin Cancer Dataset'' and ``Automated
              Dermatological Diagnosis: Hype or Reality?'' highlight the
              opportunities, hurdles, and possible pitfalls with the
              development of tools that allow for automated skin lesion
              classification. The potential clinical impact of these advances
              relies on their scalability, accuracy, and generalizability
              across a range of diagnostic scenarios.",
  journal  = "J. Invest. Dermatol.",
  volume   =  138,
  number   =  10,
  pages    = "2108--2110",
  month    =  oct,
  year     =  2018,
  language = "en"
}

@ARTICLE{Yu2009-ip,
  title    = "Are figure legends sufficient? Evaluating the contribution of
              associated text to biomedical figure comprehension",
  author   = "Yu, Hong and Agarwal, Shashank and Johnston, Mark and Cohen,
              Aaron",
  abstract = "BACKGROUND: Biomedical scientists need to access figures to
              validate research facts and to formulate or to test novel
              research hypotheses. However, figures are difficult to comprehend
              without associated text (e.g., figure legend and other reference
              text). We are developing automated systems to extract the
              relevant explanatory information along with figures extracted
              from full text articles. Such systems could be very useful in
              improving figure retrieval and in reducing the workload of
              biomedical scientists, who otherwise have to retrieve and read
              the entire full-text journal article to determine which figures
              are relevant to their research. As a crucial step, we studied the
              importance of associated text in biomedical figure comprehension.
              METHODS: Twenty subjects evaluated three figure-text
              combinations: figure+legend, figure+legend+title+abstract, and
              figure+full-text. Using a Likert scale, each subject scored each
              figure+text according to the extent to which the subject thought
              he/she understood the meaning of the figure and the confidence in
              providing the assigned score. Additionally, each subject entered
              a free text summary for each figure-text. We identified missing
              information using indicator words present within the text
              summaries. Both the Likert scores and the missing information
              were statistically analyzed for differences among the figure-text
              types. We also evaluated the quality of text summaries with the
              text-summarization evaluation method the ROUGE score. RESULTS:
              Our results showed statistically significant differences in
              figure comprehension when varying levels of text were provided.
              When the full-text article is not available, presenting just the
              figure+legend left biomedical researchers lacking 39-68\% of the
              information about a figure as compared to having complete figure
              comprehension; adding the title and abstract improved the
              situation, but still left biomedical researchers missing 30\% of
              the information. When the full-text article is available, figure
              comprehension increased to 86-97\%; this indicates that
              researchers felt that only 3-14\% of the necessary information
              for full figure comprehension was missing when full text was
              available to them. Clearly there is information in the abstract
              and in the full text that biomedical scientists deem important
              for understanding the figures that appear in full-text biomedical
              articles. CONCLUSION: We conclude that the texts that appear in
              full-text biomedical articles are useful for understanding the
              meaning of a figure, and an effective figure-mining system needs
              to unlock the information beyond figure legend. Our work provides
              important guidance to the figure mining systems that extract
              information only from figure and figure legend.",
  journal  = "J. Biomed. Discov. Collab.",
  volume   =  4,
  pages    = "1",
  month    =  jan,
  year     =  2009,
  language = "en"
}

@ARTICLE{Stirling2021-ov,
  title     = "{CellProfiler} Analyst 3.0: Accessible data exploration and
               machine learning for image analysis",
  author    = "Stirling, David R and Carpenter, Anne E and Cimini, Beth A",
  abstract  = ": Image-based experiments can yield many thousands of individual
               measurements describing each object of interest, such as cells
               in microscopy screens. CellProfiler Analyst is a free,
               open-source software package designed for the exploration of
               quantitative image-derived data and the training of machine
               learning classifiers with an intuitive user interface. We have
               now released CellProfiler Analyst 3.0, which in addition to
               enhanced performance adds support for neural network
               classifiers, identifying rare object subsets, and direct
               transfer of objects of interest from visualisation tools into
               the Classifier tool for use as training data. This release also
               increases interoperability with the recently released
               CellProfiler 4, making it easier for users to detect and measure
               particular classes of objects in their analyses. AVAILABILITY:
               CellProfiler Analyst binaries for Windows and MacOS are freely
               available for download at https://cellprofileranalyst.org/.
               Source code is implemented in Python 3 and is available at
               https://github.com/CellProfiler/CellProfiler-Analyst/. A sample
               data set is available at
               https://cellprofileranalyst.org/examples, based on images freely
               available from the Broad Bioimage Benchmark Collection (BBBC).",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press",
  volume    =  37,
  number    =  21,
  pages     = "3992--3994",
  month     =  sep,
  year      =  2021,
  language  = "en"
}

@ARTICLE{Eulenberg2017-ax,
  title    = "Reconstructing cell cycle and disease progression using deep
              learning",
  author   = "Eulenberg, Philipp and K{\"o}hler, Niklas and Blasi, Thomas and
              Filby, Andrew and Carpenter, Anne E and Rees, Paul and Theis,
              Fabian J and Wolf, F Alexander",
  abstract = "We show that deep convolutional neural networks combined with
              nonlinear dimension reduction enable reconstructing biological
              processes based on raw image data. We demonstrate this by
              reconstructing the cell cycle of Jurkat cells and disease
              progression in diabetic retinopathy. In further analysis of
              Jurkat cells, we detect and separate a subpopulation of dead
              cells in an unsupervised manner and, in classifying discrete cell
              cycle stages, we reach a sixfold reduction in error rate compared
              to a recent approach based on boosting on image features. In
              contrast to previous methods, deep learning based predictions are
              fast enough for on-the-fly analysis in an imaging flow
              cytometer.The interpretation of information-rich, high-throughput
              single-cell data is a challenge requiring sophisticated
              computational tools. Here the authors demonstrate a deep
              convolutional neural network that can classify cell cycle status
              on-the-fly.",
  journal  = "Nat. Commun.",
  volume   =  8,
  number   =  1,
  pages    = "463",
  month    =  sep,
  year     =  2017,
  language = "en"
}

@ARTICLE{Jones2009-zz,
  title    = "Scoring diverse cellular morphologies in image-based screens with
              iterative feedback and machine learning",
  author   = "Jones, Thouis R and Carpenter, Anne E and Lamprecht, Michael R
              and Moffat, Jason and Silver, Serena J and Grenier, Jennifer K
              and Castoreno, Adam B and Eggert, Ulrike S and Root, David E and
              Golland, Polina and Sabatini, David M",
  abstract = "Many biological pathways were first uncovered by identifying
              mutants with visible phenotypes and by scoring every sample in a
              screen via tedious and subjective visual inspection. Now,
              automated image analysis can effectively score many phenotypes.
              In practical application, customizing an image-analysis algorithm
              or finding a sufficient number of example cells to train a
              machine learning algorithm can be infeasible, particularly when
              positive control samples are not available and the phenotype of
              interest is rare. Here we present a supervised machine learning
              approach that uses iterative feedback to readily score multiple
              subtle and complex morphological phenotypes in high-throughput,
              image-based screens. First, automated cytological profiling
              extracts hundreds of numerical descriptors for every cell in
              every image. Next, the researcher generates a rule (i.e.,
              classifier) to recognize cells with a phenotype of interest
              during a short, interactive training session using iterative
              feedback. Finally, all of the cells in the experiment are
              automatically classified and each sample is scored based on the
              presence of cells displaying the phenotype. By using this
              approach, we successfully scored images in RNA interference
              screens in 2 organisms for the prevalence of 15 diverse cellular
              morphologies, some of which were previously intractable.",
  journal  = "Proc. Natl. Acad. Sci. U. S. A.",
  volume   =  106,
  number   =  6,
  pages    = "1826--1831",
  month    =  feb,
  year     =  2009,
  language = "en"
}

@ARTICLE{Aaron2018-qi,
  title    = "Image co-localization - co-occurrence versus correlation",
  author   = "Aaron, Jesse S and Taylor, Aaron B and Chew, Teng-Leong",
  abstract = "Fluorescence image co-localization analysis is widely utilized to
              suggest biomolecular interaction. However, there exists some
              confusion as to its correct implementation and interpretation. In
              reality, co-localization analysis consists of at least two
              distinct sets of methods, termed co-occurrence and correlation.
              Each approach has inherent and often contrasting strengths and
              weaknesses. Yet, neither one can be considered to always be
              preferable for any given application. Rather, each method is most
              appropriate for answering different types of biological question.
              This Review discusses the main factors affecting multicolor image
              co-occurrence and correlation analysis, while giving insight into
              the types of biological behavior that are better suited to one
              approach or the other. Further, the limits of pixel-based
              co-localization analysis are discussed in the context of
              increasingly popular super-resolution imaging techniques.",
  journal  = "J. Cell Sci.",
  volume   =  131,
  number   =  3,
  month    =  feb,
  year     =  2018,
  keywords = "Co-localization; Fluorescence microscopy; Image analysis;
              Manders; Pearson",
  language = "en"
}

@BOOK{Miura2016-wq,
  title     = "Bioimage Data Analysis",
  editor    = "Miura, Kota",
  publisher = "Wiley-VCH",
  year      =  2016
}

@ARTICLE{Janssen2022-bm,
  title    = "Current Methods and Pipelines for {Image-Based} Quantitation of
              Nuclear Shape and Nuclear Envelope Abnormalities",
  author   = "Janssen, Anne F J and Breusegem, Sophia Y and Larrieu, Delphine",
  abstract = "Any given cell type has an associated ``normal'' nuclear
              morphology, which is important to maintain proper cellular
              functioning and safeguard genomic integrity. Deviations from this
              can be indicative of diseases such as cancer or premature aging
              syndrome. To accurately assess nuclear abnormalities, it is
              important to use quantitative measures of nuclear morphology.
              Here, we give an overview of several nuclear abnormalities,
              including micronuclei, nuclear envelope invaginations, blebs and
              ruptures, and review the current methods used for image-based
              quantification of these abnormalities. We discuss several
              parameters that can be used to quantify nuclear shape and compare
              their outputs using example images. In addition, we present new
              pipelines for quantitative analysis of nuclear blebs and
              invaginations. Quantitative analyses of nuclear aberrations and
              shape will be important in a wide range of applications, from
              assessments of cancer cell anomalies to studies of nucleus
              deformability under mechanical or other types of stress.",
  journal  = "Cells",
  volume   =  11,
  number   =  3,
  month    =  jan,
  year     =  2022,
  keywords = "lamina; nuclear envelope; nuclear envelope abnormality; nuclear
              morphology",
  language = "en"
}

@ARTICLE{Belevich2016-vi,
  title    = "Microscopy Image Browser: A Platform for Segmentation and
              Analysis of Multidimensional Datasets",
  author   = "Belevich, Ilya and Joensuu, Merja and Kumar, Darshan and Vihinen,
              Helena and Jokitalo, Eija",
  abstract = "Understanding the structure-function relationship of cells and
              organelles in their natural context requires multidimensional
              imaging. As techniques for multimodal 3-D imaging have become
              more accessible, effective processing, visualization, and
              analysis of large datasets are posing a bottleneck for the
              workflow. Here, we present a new software package for
              high-performance segmentation and image processing of
              multidimensional datasets that improves and facilitates the full
              utilization and quantitative analysis of acquired data, which is
              freely available from a dedicated website. The open-source
              environment enables modification and insertion of new plug-ins to
              customize the program for specific needs. We provide practical
              examples of program features used for processing, segmentation
              and analysis of light and electron microscopy datasets, and
              detailed tutorials to enable users to rapidly and thoroughly
              learn how to use the program.",
  journal  = "PLoS Biol.",
  volume   =  14,
  number   =  1,
  pages    = "e1002340",
  month    =  jan,
  year     =  2016,
  language = "en"
}

@ARTICLE{Stringer2021-uq,
  title    = "Cellpose: a generalist algorithm for cellular segmentation",
  author   = "Stringer, Carsen and Wang, Tim and Michaelos, Michalis and
              Pachitariu, Marius",
  abstract = "Many biological applications require the segmentation of cell
              bodies, membranes and nuclei from microscopy images. Deep
              learning has enabled great progress on this problem, but current
              methods are specialized for images that have large training
              datasets. Here we introduce a generalist, deep learning-based
              segmentation method called Cellpose, which can precisely segment
              cells from a wide range of image types and does not require model
              retraining or parameter adjustments. Cellpose was trained on a
              new dataset of highly varied images of cells, containing over
              70,000 segmented objects. We also demonstrate a three-dimensional
              (3D) extension of Cellpose that reuses the two-dimensional (2D)
              model and does not require 3D-labeled data. To support community
              contributions to the training data, we developed software for
              manual labeling and for curation of the automated results.
              Periodically retraining the model on the community-contributed
              data will ensure that Cellpose improves constantly.",
  journal  = "Nat. Methods",
  volume   =  18,
  number   =  1,
  pages    = "100--106",
  month    =  jan,
  year     =  2021,
  language = "en"
}

@ARTICLE{Emami2021-az,
  title    = "Computerized cell tracking: Current methods, tools and challenges",
  author   = "Emami, Neda and Sedaei, Zahra and Ferdousi, Reza",
  abstract = "In developmental biology, knowledge of cell structure and their
              (morpho) dynamic behavior, leads to a comprehensive understanding
              of their conducts and the mechanisms in which they participate.
              This knowledge is a decisive factor in biological research and
              also in all drug development steps, medicinal or preventive
              therapies. Experimental cell analysis is hard, expensive, and
              time-consuming. To overcome these difficulties, in recent years,
              several computational object tracking methods, software system
              and packages have been developed in cell sciences that bring
              together different disciplines and branches of technologies.
              Object tracking is the process of locating and monitoring
              specific object and its behavior in sequential images. In this
              paper, a comprehensive review on object tracking stages and
              computational methods that are utilized in terms of cell tracking
              has been organized. Besides, the available software packages and
              toolkits, challenges, and their solution in time lapse microscopy
              images in this scope were reviewed. The aim of describing
              computational cell tracking methods and tools is that biologist
              and cell scientists might take advantage of these computational
              techniques to find another method to gain complementary
              information for their question of interest.",
  journal  = "Visual Informatics",
  volume   =  5,
  number   =  1,
  pages    = "1--13",
  month    =  mar,
  year     =  2021,
  keywords = "Cell tracking; Digital cell tracking; Live-cell microscopy; Image
              analysis; Bioimage informatics; Subcellular location"
}

@ARTICLE{Tinevez2017-fb,
  title    = "{TrackMate}: An open and extensible platform for single-particle
              tracking",
  author   = "Tinevez, Jean-Yves and Perry, Nick and Schindelin, Johannes and
              Hoopes, Genevieve M and Reynolds, Gregory D and Laplantine,
              Emmanuel and Bednarek, Sebastian Y and Shorte, Spencer L and
              Eliceiri, Kevin W",
  abstract = "We present TrackMate, an open source Fiji plugin for the
              automated, semi-automated, and manual tracking of
              single-particles. It offers a versatile and modular solution that
              works out of the box for end users, through a simple and
              intuitive user interface. It is also easily scriptable and
              adaptable, operating equally well on 1D over time, 2D over time,
              3D over time, or other single and multi-channel image variants.
              TrackMate provides several visualization and analysis tools that
              aid in assessing the relevance of results. The utility of
              TrackMate is further enhanced through its ability to be readily
              customized to meet specific tracking problems. TrackMate is an
              extensible platform where developers can easily write their own
              detection, particle linking, visualization or analysis algorithms
              within the TrackMate environment. This evolving framework
              provides researchers with the opportunity to quickly develop and
              optimize new algorithms based on existing TrackMate modules
              without the need of having to write de novo user interfaces,
              including visualization, analysis and exporting tools. The
              current capabilities of TrackMate are presented in the context of
              three different biological problems. First, we perform
              Caenorhabditis-elegans lineage analysis to assess how
              light-induced damage during imaging impairs its early
              development. Our TrackMate-based lineage analysis indicates the
              lack of a cell-specific light-sensitive mechanism. Second, we
              investigate the recruitment of NEMO (NF-$\kappa$B essential
              modulator) clusters in fibroblasts after stimulation by the
              cytokine IL-1 and show that photodamage can generate artifacts in
              the shape of TrackMate characterized movements that confuse
              motility analysis. Finally, we validate the use of TrackMate for
              quantitative lifetime analysis of clathrin-mediated endocytosis
              in plant cells.",
  journal  = "Methods",
  volume   =  115,
  pages    = "80--90",
  month    =  feb,
  year     =  2017,
  keywords = "Clathin-mediated endocytosis; Image analysis; Microscopy;
              Open-source software; Phototoxicity; Single-particle tracking",
  language = "en"
}

@ARTICLE{Blenman2021-ki,
  title    = "{ISAC} Probe Tag Dictionary: Standardized Nomenclature for
              Detection and Visualization Labels Used in Cytometry and
              Microscopy Imaging",
  author   = "Blenman, Kim R M and Spidlen, Josef and Parks, David R and Moore,
              Wayne and Treister, Adam and Leif, Robert and Bray, Chris and
              Goldberg, Michael and {ISAC Data Standards Task Force} and
              Brinkman, Ryan",
  abstract = "Since the advent of microscopy imaging and flow cytometry, there
              has been an explosion in the number of probes, consisting of a
              component binding to an analyte and a detectable tag, to mark
              areas of interest in or on cells and tissue. Probe tags have been
              created to detect and/or visualize probes. Over time, these probe
              tags have increased in number. The expansion has resulted in
              arbitrarily created synonyms of probe tags used in publications
              and software. The synonyms are problematic for readability of
              publications, accuracy of text/data mining, and bridging data
              from multiple platforms, protocols, and databases for Big Data
              analysis. Development and implementation of a universal language
              for probe tags will ensure equivalent quality and level of data
              being reported or extracted for clinical/scientific evaluation as
              well as help connect data from many platforms. The International
              Society for Advancement of Cytometry Data Standards Task Force
              composed of academic scientists and industry
              hardware/software/reagent manufactures have developed
              recommendations for a standardized nomenclature for probe tags
              used in cytometry and microscopy imaging. These recommendations
              are shared in this technical note in the form of a Probe Tag
              Dictionary. \copyright{} 2020 International Society for
              Advancement of Cytometry.",
  journal  = "Cytometry A",
  volume   =  99,
  number   =  1,
  pages    = "103--106",
  month    =  jan,
  year     =  2021,
  keywords = "FCS; bioinformatics; data standards; dictionary; flow cytometry;
              labels; microscopy imaging; nomenclature; probe tag",
  language = "en"
}

@ARTICLE{Sheen2019-bg,
  title    = "Replication Study: Biomechanical remodeling of the
              microenvironment by stromal caveolin-1 favors tumor invasion and
              metastasis",
  author   = "Sheen, Mee Rie and Fields, Jennifer L and Northan, Brian and
              Lacoste, Judith and Ang, Lay-Hong and Fiering, Steven and
              {Reproducibility Project: Cancer Biology}",
  abstract = "As part of the Reproducibility Project: Cancer Biology we
              published a Registered Report (Fiering et al., 2015) that
              described how we intended to replicate selected experiments from
              the paper 'Biomechanical remodeling of the microenvironment by
              stromal caveolin-1 favors tumor invasion and metastasis' (Goetz
              et al., 2011). Here we report the results. Primary mouse
              embryonic fibroblasts (pMEFs) expressing caveolin 1 (Cav1WT)
              demonstrated increased extracellular matrix remodeling in vitro
              compared to Cav1 deficient (Cav1KO) pMEFs, similar to the
              original study (Goetz et al., 2011). In vivo, we found higher
              levels of intratumoral stroma remodeling, determined by
              fibronectin fiber orientation, in tumors from cancer cells
              co-injected with Cav1WT pMEFs compared to cancer cells only or
              cancer cells plus Cav1KO pMEFs, which were in the same direction
              as the original study (Supplemental Figure S7C; Goetz et al.,
              2011), but not statistically significant. Primary tumor growth
              was similar between conditions, like the original study
              (Supplemental Figure S7Ca; Goetz et al., 2011). We found
              metastatic burden was similar between Cav1WT and Cav1KO pMEFs,
              while the original study found increased metastases with Cav1WT
              (Figure 7C; Goetz et al., 2011); however, the duration of our in
              vivo experiments (45 days) were much shorter than in the study by
              Goetz et al. (2011) (75 days). This makes it difficult to
              interpret the difference between the studies as it is possible
              that the cells required more time to manifest the difference
              between treatments observed by Goetz et al. We also found a
              statistically significant negative correlation of intratumoral
              remodeling with metastatic burden, while the original study found
              a statistically significant positive correlation (Figure 7Cd;
              Goetz et al., 2011), but again there were differences between the
              studies in terms of the duration of the metastasis studies and
              the imaging approaches that could have impacted the outcomes.
              Finally, we report meta-analyses for each result.",
  journal  = "Elife",
  volume   =  8,
  month    =  dec,
  year     =  2019,
  keywords = "Reproducibility Project: Cancer Biology; biochemical remodeling;
              cancer biology; human; metascience; mouse; replication;
              reproducibility; tumor microenvironment",
  language = "en"
}

@ARTICLE{Burges2010-fi,
  title     = "Dimension Reduction: A Guided Tour",
  author    = "Burges, Christopher J C",
  abstract  = "We give a tutorial overview of several geometric methods for
               dimension reduction. We divide the methods into projective
               methods and meth- ods that model the manifold on which the data
               lies. For projective methods, we review projection pursuit,
               principal component analysis (PCA), kernel PCA, probabilistic
               PCA, canonical correlation analysis, oriented PCA, and several
               techniques for sufficient dimension reduc- tion. For the
               manifold methods, we review multidimensional scaling (MDS),
               landmark MDS, Isomap, locally linear embedding, Laplacian
               eigenmaps and spectral clustering. The Nystrom method, which
               links several of the manifold algorithms, is also reviewed. The
               goal is to pro- vide a self-contained overview of key concepts
               underlying many of these algorithms, and to give pointers for
               further reading.",
  journal   = "Foundations and Trends\textregistered{} in Machine Learning",
  publisher = "Now Publishers",
  volume    =  2,
  number    =  4,
  month     =  jan,
  year      =  2010
}

@ARTICLE{Goh2017-kd,
  title    = "Why Batch Effects Matter in Omics Data, and How to Avoid Them",
  author   = "Goh, Wilson Wen Bin and Wang, Wei and Wong, Limsoon",
  abstract = "Effective integration and analysis of new high-throughput data,
              especially gene-expression and proteomic-profiling data, are
              expected to deliver novel clinical insights and therapeutic
              options. Unfortunately, technical heterogeneity or batch effects
              (different experiment times, handlers, reagent lots, etc.) have
              proven challenging. Although batch effect-correction algorithms
              (BECAs) exist, we know little about effective batch-effect
              mitigation: even now, new batch effect-associated problems are
              emerging. These include false effects due to misapplying BECAs
              and positive bias during model evaluations. Depending on the
              choice of algorithm and experimental set-up, biological
              heterogeneity can be mistaken for batch effects and wrongfully
              removed. Here, we examine these emerging batch effect-associated
              problems, propose a series of best practices, and discuss some of
              the challenges that lie ahead.",
  journal  = "Trends Biotechnol.",
  volume   =  35,
  number   =  6,
  pages    = "498--507",
  month    =  jun,
  year     =  2017,
  keywords = "batch effect; cross-validation; data integration; heterogeneity;
              reproducibility",
  language = "en"
}

@ARTICLE{Leek2012-rv,
  title    = "The sva package for removing batch effects and other unwanted
              variation in high-throughput experiments",
  author   = "Leek, Jeffrey T and Johnson, W Evan and Parker, Hilary S and
              Jaffe, Andrew E and Storey, John D",
  abstract = "Heterogeneity and latent variables are now widely recognized as
              major sources of bias and variability in high-throughput
              experiments. The most well-known source of latent variation in
              genomic experiments are batch effects-when samples are processed
              on different days, in different groups or by different people.
              However, there are also a large number of other variables that
              may have a major impact on high-throughput measurements. Here we
              describe the sva package for identifying, estimating and removing
              unwanted sources of variation in high-throughput experiments. The
              sva package supports surrogate variable estimation with the sva
              function, direct adjustment for known batch effects with the
              ComBat function and adjustment for batch and latent variables in
              prediction problems with the fsva function.",
  journal  = "Bioinformatics",
  volume   =  28,
  number   =  6,
  pages    = "882--883",
  month    =  mar,
  year     =  2012,
  language = "en"
}

@ARTICLE{North2006-sb,
    author = {North , Alison J. },
    title = "{Seeing is believing? A beginners' guide to practical pitfalls in image acquisition }",
    journal = {Journal of Cell Biology},
    volume = {172},
    number = {1},
    pages = {9-18},
    year = {2006},
    month = {01},
    abstract = "{Imaging can be thought of as the most direct of experiments. You see something; you report what you see. If only things were truly this simple. Modern imaging technology has brought about a revolution in the kinds of questions we can approach, but this comes at the price of increasingly complex equipment. Moreover, in an attempt to market competing systems, the microscopes have often been inappropriately described as easy to use and suitable for near-beginners. Insufficient understanding of the experimental manipulations and equipment set-up leads to the introduction of errors during image acquisition. In this feature, I review some of the most common practical pitfalls faced by researchers during image acquisition, and how they can affect the interpretation of the experimental data. This article is targeted neither to the microscopy gurus who push forward the frontiers of imaging technology nor to my imaging specialist colleagues who may wince at the overly simplistic comments and lack of detail. Instead, this is for beginners who gulp with alarm when they hear the word “confocal pinhole” or sigh as they watch their cells fade and die in front of their very eyes time and time again at the microscope. Take heart, beginners, if microscopes were actually so simple then many people (including myself) would suddenly be out of a job! }",
    issn = {0021-9525},
    doi = {10.1083/jcb.200507103},
    url = {https://doi.org/10.1083/jcb.200507103},
}

@ARTICLE{Stadler2013,
author={Stadler, Charlotte
and Rexhepaj, Elton
and Singan, Vasanth R.
and Murphy, Robert F.
and Pepperkok, Rainer
and Uhl{\'e}n, Mathias
and Simpson, Jeremy C.
and Lundberg, Emma},
title={Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells},
journal={Nature Methods},
year={2013},
month={Apr},
day={01},
volume={10},
number={4},
pages={315-323},
abstract={In this analysis, the authors directly compared immunofluorescence and fluorescent-protein tagging of 506 human proteins and studied their subcellular localization. They conclude that the two methodologies are highly complementary and propose an integrative strategy for the characterization of newly identified proteins.},
issn={1548-7105},
doi={10.1038/nmeth.2377},
url={https://doi.org/10.1038/nmeth.2377}
}

@ARTICLE{Stockley2017,
author={Stockley, John H.
and Evans, Kimberley
and Matthey, Moritz
and Volbracht, Katrin
and Agathou, Sylvia
and Mukanowa, Jana
and Burrone, Juan
and K{\'a}rad{\'o}ttir, Ragnhildur T.},
title={Surpassing light-induced cell damage in vitro with novel cell culture media},
journal={Scientific Reports},
year={2017},
month={Apr},
day={12},
volume={7},
number={1},
pages={849},
abstract={Light is extensively used to study cells in real time (live cell imaging), separate cells using fluorescence activated cell sorting (FACS) and control cellular functions with light sensitive proteins (Optogenetics). However, photo-sensitive molecules inside cells and in standard cell culture media generate toxic by-products that interfere with cellular functions and cell viability when exposed to light. Here we show that primary cells from the rat central nervous system respond differently to photo-toxicity, in that astrocytes and microglia undergo morphological changes, while in developing neurons and oligodendrocyte progenitor cells (OPCs) it induces cellular death. To prevent photo-toxicity and to allow for long-term photo-stimulation without causing cellular damage, we formulated new photo-inert media called MEMO and NEUMO, and an antioxidant rich and serum free supplement called SOS. These new media reduced the detrimental effects caused by light and allowed cells to endure up to twenty times more light exposure without adverse effects, thus bypassing the optical constraints previously limiting experiments.},
issn={2045-2322},
doi={10.1038/s41598-017-00829-x},
url={https://doi.org/10.1038/s41598-017-00829-x}
}

@ARTICLE{Sittewelle2023,
    author = {Sittewelle, Méghane and Ferrandiz, Nuria and Fesenko, Mary and Royle, Stephen J.},
    title = "{Genetically encoded imaging tools for investigating cell dynamics at a glance}",
    journal = {Journal of Cell Science},
    volume = {136},
    number = {7},
    year = {2023},
    month = {04},
    abstract = "{The biology of a cell is the sum of many highly dynamic processes, each orchestrated by a plethora of proteins and other molecules. Microscopy is an invaluable approach to spatially and temporally dissect the molecular details of these processes. Hundreds of genetically encoded imaging tools have been developed that allow cell scientists to determine the function of a protein of interest in the context of these dynamic processes. Broadly, these tools fall into three strategies: observation, inhibition and activation. Using examples for each strategy, in this Cell Science at a Glance and the accompanying poster, we provide a guide to using these tools to dissect protein function in a given cellular process. Our focus here is on tools that allow rapid modification of proteins of interest and how observing the resulting changes in cell states is key to unlocking dynamic cell processes. The aim is to inspire the reader's next set of imaging experiments.}",
    issn = {0021-9533},
    doi = {10.1242/jcs.260783},
    url = {https://doi.org/10.1242/jcs.260783},
    note = {jcs260783}
}

@ARTICLE{GarciaFossaCruz2023,
author = {Garcia-Fossa, Fernanda and Cruz, Mario Costa and Haghighi, Marzieh and de Jesus, Marcelo Bispo and Singh, Shantanu and Carpenter, Anne E. and Cimini, Beth A.},
title = {Interpreting Image-based Profiles using Similarity Clustering and Single-Cell Visualization},
journal = {Current Protocols},
volume = {3},
number = {3},
pages = {e713},
keywords = {high-dimensional data, image-based profiling, Morpheus, morphological analysis, profiling, single-cell visualization},
doi = {https://doi.org/10.1002/cpz1.713},
url = {https://currentprotocols.onlinelibrary.wiley.com/doi/abs/10.1002/cpz1.713},
abstract = {Abstract Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. Here, we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanisms of action. The protocol includes steps to examine feature-driving differences between samples and to visualize correlations between features and treatments to create interpretable heatmaps using the open-source web tool Morpheus. In the second protocol, we show how to interactively explore images together with the numerical data, and we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. Together, these two tutorials help researchers interpret image-based data to speed up research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory analysis of profile similarities and driving features Basic Protocol 2: Image and single-cell visualization following profile interpretation},
year = {2023}
}

@Article{Lever2017-pca,
title = "Principal component analysis",
author = "Lever, Jake and Krzywinski, Martin and Altman, Naomi",
journal = "Nat. Methods",
volume = 14,
pages = "641--642",
month = jul,
year = 2017,
language = "en"
}


@ARTICLE{Senft2023-zy,
  title     = "A biologist's guide to planning and performing quantitative
               bioimaging experiments",
  author    = "Senft, Rebecca A and Diaz-Rohrer, Barbara and Colarusso, Pina
               and Swift, Lucy and Jamali, Nasim and Jambor, Helena and Pengo,
               Thomas and Brideau, Craig and Llopis, Paula Montero and Uhlmann,
               Virginie and Kirk, Jason and Gonzales, Kevin Andrew and
               Bankhead, Peter and Evans, III, Edward L and Eliceiri, Kevin W
               and Cimini, Beth A",
  abstract  = "Technological advancements in biology and microscopy have
               empowered a transition from bioimaging as an observational
               method to a quantitative one. However, as biologists are
               adopting quantitative bioimaging and these experiments become
               more complex, researchers need additional expertise to carry out
               this work in a rigorous and reproducible manner. This Essay
               provides a navigational guide for experimental biologists to aid
               understanding of quantitative bioimaging from sample preparation
               through to image acquisition, image analysis, and data
               interpretation. We discuss the interconnectedness of these
               steps, and for each, we provide general recommendations, key
               questions to consider, and links to high-quality open-access
               resources for further learning. This synthesis of information
               will empower biologists to plan and execute rigorous
               quantitative bioimaging experiments efficiently.",
  journal   = "PLoS Biol.",
  publisher = "Public Library of Science",
  volume    =  21,
  number    =  6,
  pages     = "e3002167",
  month     =  jun,
  year      =  2023
}


@ARTICLE{Lee2024-zu,
  title    = "Believing is seeing - the deceptive influence of bias in
              quantitative microscopy",
  author   = "Lee, Rachel M and Eisenman, Leanna R and Khuon, Satya and Aaron,
              Jesse S and Chew, Teng-Leong",
  abstract = "The visual allure of microscopy makes it an intuitively powerful
              research tool. Intuition, however, can easily obscure or distort
              the reality of the information contained in an image. Common
              cognitive biases, combined with institutional pressures that
              reward positive research results, can quickly skew a microscopy
              project towards upholding, rather than rigorously challenging, a
              hypothesis. The impact of these biases on a variety of research
              topics is well known. What might be less appreciated are the many
              forms in which bias can permeate a microscopy experiment. Even
              well-intentioned researchers are susceptible to bias, which must
              therefore be actively recognized to be mitigated. Importantly,
              although image quantification has increasingly become an
              expectation, ostensibly to confront subtle biases, it is not a
              guarantee against bias and cannot alone shield an experiment from
              cognitive distortions. Here, we provide illustrative examples of
              the insidiously pervasive nature of bias in microscopy
              experiments - from initial experimental design to image
              acquisition, analysis and data interpretation. We then provide
              suggestions that can serve as guard rails against bias.",
  journal  = "J. Cell Sci.",
  volume   =  137,
  number   =  1,
  month    =  jan,
  year     =  2024,
  keywords = "Bias; Bioimage analysis; Microscopy; Quantitative microscopy",
  language = "en"
}

@ARTICLE{Weisbart2023-kc,
  title    = "{CellProfiler} plugins - An easy image analysis platform
              integration for containers and Python tools",
  author   = "Weisbart, Erin and Tromans-Coia, Callum and Diaz-Rohrer, Barbara
              and Stirling, David R and Garcia-Fossa, Fernanda and Senft,
              Rebecca A and Hiner, Mark C and de Jesus, Marcelo B and Eliceiri,
              Kevin W and Cimini, Beth A",
  abstract = "CellProfiler is a widely used software for creating reproducible,
              reusable image analysis workflows without needing to code. In
              addition to the >90 modules that make up the main CellProfiler
              program, CellProfiler has a plugins system that allows for the
              creation of new modules which integrate with other Python tools
              or tools that are packaged in software containers. The
              CellProfiler-plugins repository contains a number of these
              CellProfiler modules, especially modules that are experimental
              and/or dependency-heavy. Here, we present an upgraded
              CellProfiler-plugins repository, an example of accessing
              containerised tools, improved documentation and added
              citation/reference tools to facilitate the use and contribution
              of the community.",
  journal  = "J. Microsc.",
  month    =  sep,
  year     =  2023,
  keywords = "CellProfiler; Python; image analysis; plugin; software; software
              container; workflow",
  language = "en"
}

@ARTICLE{Stringer2024-gd,
  title    = "Cellpose3: one-click image restoration for improved cellular
              segmentation",
  author   = "Stringer, Carsen and Pachitariu, Marius",
  abstract = "Generalist methods for cellular segmentation have good
              out-of-the-box performance on a variety of image types. However,
              existing methods struggle for images that are degraded by noise,
              blurred or undersampled, all of which are common in microscopy.
              We focused the development of Cellpose3 on addressing these
              cases, and here we demonstrate substantial out-of-the-box gains
              in segmentation and image quality for noisy, blurry or
              undersampled images. Unlike previous approaches, which train
              models to restore pixel values, we trained Cellpose3 to output
              images that are well-segmented by a generalist segmentation
              model, while maintaining perceptual similarity to the target
              images. Furthermore, we trained the restoration models on a
              large, varied collection of datasets, thus ensuring good
              generalization to user images. We provide these tools as
              ``one-click'' buttons inside the graphical interface of Cellpose
              as well as in the Cellpose API. \#\#\# Competing Interest
              Statement The authors have declared no competing interest.",
  journal  = "bioRxiv",
  pages    = "2024.02.10.579780",
  month    =  feb,
  year     =  2024,
  language = "en"
}

@ARTICLE{Ouyang2022-fm,
  title    = "{BioImage} Model Zoo: A {Community-Driven} Resource for
              Accessible Deep Learning in {BioImage} Analysis",
  author   = "Ouyang, Wei and Beuttenmueller, Fynn and G{\'o}mez-de-Mariscal,
              Estibaliz and Pape, Constantin and Burke, Tom and
              Garcia-L{\'o}pez-de-Haro, Carlos and Russell, Craig and
              Moya-Sans, Luc{\'\i}a and de-la-Torre-Guti{\'e}rrez, Cristina and
              Schmidt, Deborah and Kutra, Dominik and Novikov, Maksim and
              Weigert, Martin and Schmidt, Uwe and Bankhead, Peter and
              Jacquemet, Guillaume and Sage, Daniel and Henriques, Ricardo and
              Mu{\~n}oz-Barrutia, Arrate and Lundberg, Emma and Jug, Florian
              and Kreshuk, Anna",
  abstract = "Deep learning-based approaches are revolutionizing imaging-driven
              scientific research. However, the accessibility and
              reproducibility of deep learning-based workflows for imaging
              scientists remain far from sufficient. Several tools have
              recently risen to the challenge of democratizing deep learning by
              providing user-friendly interfaces to analyze new data with
              pre-trained or fine-tuned models. Still, few of the existing
              pre-trained models are interoperable between these tools,
              critically restricting a model's overall utility and the
              possibility of validating and reproducing scientific analyses.
              Here, we present the BioImage Model Zoo (): a community-driven,
              fully open resource where standardized pre-trained models can be
              shared, explored, tested, and downloaded for further adaptation
              or direct deployment in multiple end user-facing tools (e.g.,
              ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic,
              CSBDeep). To enable everyone to contribute and consume the Zoo
              resources, we provide a model standard to enable
              cross-compatibility, a rich list of example models and practical
              use-cases, developer tools, documentation, and the accompanying
              infrastructure for model upload, download and testing. Our
              contribution aims to lay the groundwork to make deep learning
              methods for microscopy imaging findable, accessible,
              interoperable, and reusable (FAIR) across software tools and
              platforms. \#\#\# Competing Interest Statement The authors have
              declared no competing interest.",
  journal  = "bioRxiv",
  pages    = "2022.06.07.495102",
  month    =  jun,
  year     =  2022,
  language = "en"
}

@ARTICLE{Moser2024-vt,
  title    = "Piximi - An Images to Discovery web tool for bioimages and beyond",
  author   = "Moser, Levin M and Gogoberidze, Nodar and Papaleo, Andréa and
              Lucas, Alice and Dao, David and Friedrich, Christoph A and
              Paavolainen, Lassi and Molnar, Csaba and Stirling, David R and
              Hung, Jane and Wang, Rex and Tromans-Coia, Callum and Li, Bin and
              Evans, Edward L and Eliceiri, Kevin W and Horvath, Peter and
              Carpenter, Anne E and Cimini, Beth A",
  journal  = "bioRxiv",
  pages    = "2024.06.03.597232",
  abstract = "Deep learning has greatly accelerated research in biological image
              analysis yet it often requires programming skills and specialized
              tool installation. Here we present Piximi, a modern,
              no-programming image analysis tool leveraging deep learning.
              Implemented as a web application at Piximi.app, Piximi requires no
              installation and can be accessed by any modern web browser. Its
              client-only architecture preserves the security of researcher data
              by running all computation locally. Piximi offers four core
              modules: a deep learning classifier, an image annotator,
              measurement modules, and pre-trained deep learning segmentation
              modules. Piximi is interoperable with existing tools and workflows
              by supporting import and export of common data and model formats.
              The intuitive researcher interface and easy access to Piximi
              allows biological researchers to obtain insights into images
              within just a few minutes. Piximi aims to bring deep
              learning-powered image analysis to a broader community by
              eliminating barriers to entry. \#\#\# Competing Interest Statement
              The authors have declared no competing interest.",
  month    =  jun,
  year     =  2024,
  language = "en"
}

@ARTICLE{726791,
  author={Lecun, Y. and Bottou, L. and Bengio, Y. and Haffner, P.},
  journal={Proceedings of the IEEE}, 
  title={Gradient-based learning applied to document recognition}, 
  year={1998},
  volume={86},
  number={11},
  pages={2278-2324},
  keywords={Neural networks;Pattern recognition;Machine learning;Optical character recognition software;Character recognition;Feature extraction;Multi-layer neural network;Optical computing;Hidden Markov models;Principal component analysis},
  doi={10.1109/5.726791}}


@ARTICLE{Bajcsy2025-kk,
  title     = "Enabling global image data sharing in the life sciences",
  author    = "Bajcsy, Peter and Bhattiprolu, Sreenivas and Börner, Katy and
               Cimini, Beth A and Collinson, Lucy and Ellenberg, Jan and Fiolka,
               Reto and Giger, Maryellen and Goscinski, Wojtek and Hartley,
               Matthew and Hotaling, Nathan and Horwitz, Rick and Jug, Florian
               and Kemmer, Isabel and Kreshuk, Anna and Lundberg, Emma and
               Mathur, Aastha and Narayan, Kedar and Onami, Shuichi and Plant,
               Anne L and Prior, Fred and Swedlow, Jason R and Taylor, Adam and
               Keppler, Antje",
  journal   = "Nat. Methods",
  publisher = "Springer Science and Business Media LLC",
  pages     = "1--5",
  abstract  = "Despite the importance of imaging in biological and medical
               research, a large body of informative and precious image data
               never sees the light of day. To ensure scientific rigor as well
               as the reuse of data for scientific discovery, image data need to
               be made FAIR (findable, accessible, interoperable and reusable).
               Image data experts are working together globally to agree on
               common data formats, metadata, ontologies and supporting tools
               toward image data FAIRification. With this Perspective, we call
               on public funders to join these efforts to support their national
               scientists. What researchers most urgently need are openly
               accessible resources for image data storage that are operated
               under long-term commitments by their funders. Although existing
               resources in Australia, Japan and Europe are already
               collaborating to enable global image data sharing, these efforts
               will fall short unless more countries invest in operating and
               federating their own open data resources. This will allow us to
               harvest the enormous potential of existing image data, preventing
               substantial loss of unrealized value from past investments in
               imaging acquisition infrastructure. This Perspective provides a
               roadmap toward globally federated open data resources for
               bioimaging data.",
  month     =  mar,
  year      =  2025,
  language  = "en"
}

@ARTICLE{Cimini2024-uh,
  title     = "The crucial role of bioimage analysts in scientific research and
               publication",
  author    = "Cimini, Beth A and Bankhead, Peter and D'Antuono, Rocco and
               Fazeli, Elnaz and Fernandez-Rodriguez, Julia and Fuster-Barceló,
               Caterina and Haase, Robert and Jambor, Helena Klara and Jones,
               Martin L and Jug, Florian and Klemm, Anna H and Kreshuk, Anna and
               Marcotti, Stefania and Martins, Gabriel G and McArdle, Sara and
               Miura, Kota and Muñoz-Barrutia, Arrate and Murphy, Laura C and
               Nelson, Michael S and Nørrelykke, Simon F and Paul-Gilloteaux,
               Perrine and Pengo, Thomas and Pylvänäinen, Joanna W and Pytowski,
               Lior and Ravera, Arianna and Reinke, Annika and Rekik, Yousr and
               Strambio-De-Castillia, Caterina and Thédié, Daniel and Uhlmann,
               Virginie and Umney, Oliver and Wiggins, Laura and Eliceiri, Kevin
               W",
  journal   = "J. Cell Sci.",
  publisher = "The Company of Biologists",
  volume    =  137,
  number    =  20,
  pages     = "jcs262322",
  abstract  = "Summary: We examine the importance of bioimage analysis,
               obstacles to wider adoption of these important methods and ways
               to ensure that bioimage analysts receive the career support they
               need.",
  month     =  oct,
  year      =  2024,
  language  = "en"
}


@ARTICLE{Reinke2024-aj,
  title    = "Understanding metric-related pitfalls in image analysis
              validation",
  author   = "Reinke, Annika and Tizabi, Minu D and Baumgartner, Michael and
              Eisenmann, Matthias and Heckmann-N{\"o}tzel, Doreen and Kavur, A
              Emre and R{\"a}dsch, Tim and Sudre, Carole H and Acion, Laura and
              Antonelli, Michela and Arbel, Tal and Bakas, Spyridon and Benis,
              Arriel and Buettner, Florian and Cardoso, M Jorge and Cheplygina,
              Veronika and Chen, Jianxu and Christodoulou, Evangelia and
              Cimini, Beth A and Farahani, Keyvan and Ferrer, Luciana and
              Galdran, Adrian and van Ginneken, Bram and Glocker, Ben and
              Godau, Patrick and Hashimoto, Daniel A and Hoffman, Michael M and
              Huisman, Merel and Isensee, Fabian and Jannin, Pierre and Kahn,
              Charles E and Kainmueller, Dagmar and Kainz, Bernhard and
              Karargyris, Alexandros and Kleesiek, Jens and Kofler, Florian and
              Kooi, Thijs and Kopp-Schneider, Annette and Kozubek, Michal and
              Kreshuk, Anna and Kurc, Tahsin and Landman, Bennett A and
              Litjens, Geert and Madani, Amin and Maier-Hein, Klaus and Martel,
              Anne L and Meijering, Erik and Menze, Bjoern and Moons, Karel G M
              and M{\"u}ller, Henning and Nichyporuk, Brennan and Nickel, Felix
              and Petersen, Jens and Rafelski, Susanne M and Rajpoot, Nasir and
              Reyes, Mauricio and Riegler, Michael A and Rieke, Nicola and
              Saez-Rodriguez, Julio and S{\'a}nchez, Clara I and Shetty,
              Shravya and Summers, Ronald M and Taha, Abdel A and Tiulpin,
              Aleksei and Tsaftaris, Sotirios A and Van Calster, Ben and
              Varoquaux, Ga{\"e}l and Yaniv, Ziv R and J{\"a}ger, Paul F and
              Maier-Hein, Lena",
  abstract = "Validation metrics are key for tracking scientific progress and
              bridging the current chasm between artificial intelligence
              research and its translation into practice. However, increasing
              evidence shows that, particularly in image analysis, metrics are
              often chosen inadequately. Although taking into account the
              individual strengths, weaknesses and limitations of validation
              metrics is a critical prerequisite to making educated choices,
              the relevant knowledge is currently scattered and poorly
              accessible to individual researchers. Based on a multistage
              Delphi process conducted by a multidisciplinary expert consortium
              as well as extensive community feedback, the present work
              provides a reliable and comprehensive common point of access to
              information on pitfalls related to validation metrics in image
              analysis. Although focused on biomedical image analysis, the
              addressed pitfalls generalize across application domains and are
              categorized according to a newly created, domain-agnostic
              taxonomy. The work serves to enhance global comprehension of a
              key topic in image analysis validation.",
  journal  = "Nat. Methods",
  volume   =  21,
  number   =  2,
  pages    = "182--194",
  month    =  feb,
  year     =  2024,
  language = "en"
}

@ARTICLE{Maier-Hein2024-cp,
  title    = "Metrics reloaded: recommendations for image analysis validation",
  author   = "Maier-Hein, Lena and Reinke, Annika and Godau, Patrick and
              Tizabi, Minu D and Buettner, Florian and Christodoulou, Evangelia
              and Glocker, Ben and Isensee, Fabian and Kleesiek, Jens and
              Kozubek, Michal and Reyes, Mauricio and Riegler, Michael A and
              Wiesenfarth, Manuel and Kavur, A Emre and Sudre, Carole H and
              Baumgartner, Michael and Eisenmann, Matthias and
              Heckmann-N{\"o}tzel, Doreen and R{\"a}dsch, Tim and Acion, Laura
              and Antonelli, Michela and Arbel, Tal and Bakas, Spyridon and
              Benis, Arriel and Blaschko, Matthew B and Cardoso, M Jorge and
              Cheplygina, Veronika and Cimini, Beth A and Collins, Gary S and
              Farahani, Keyvan and Ferrer, Luciana and Galdran, Adrian and van
              Ginneken, Bram and Haase, Robert and Hashimoto, Daniel A and
              Hoffman, Michael M and Huisman, Merel and Jannin, Pierre and
              Kahn, Charles E and Kainmueller, Dagmar and Kainz, Bernhard and
              Karargyris, Alexandros and Karthikesalingam, Alan and Kofler,
              Florian and Kopp-Schneider, Annette and Kreshuk, Anna and Kurc,
              Tahsin and Landman, Bennett A and Litjens, Geert and Madani, Amin
              and Maier-Hein, Klaus and Martel, Anne L and Mattson, Peter and
              Meijering, Erik and Menze, Bjoern and Moons, Karel G M and
              M{\"u}ller, Henning and Nichyporuk, Brennan and Nickel, Felix and
              Petersen, Jens and Rajpoot, Nasir and Rieke, Nicola and
              Saez-Rodriguez, Julio and S{\'a}nchez, Clara I and Shetty,
              Shravya and van Smeden, Maarten and Summers, Ronald M and Taha,
              Abdel A and Tiulpin, Aleksei and Tsaftaris, Sotirios A and Van
              Calster, Ben and Varoquaux, Ga{\"e}l and J{\"a}ger, Paul F",
  abstract = "Increasing evidence shows that flaws in machine learning (ML)
              algorithm validation are an underestimated global problem. In
              biomedical image analysis, chosen performance metrics often do
              not reflect the domain interest, and thus fail to adequately
              measure scientific progress and hinder translation of ML
              techniques into practice. To overcome this, we created Metrics
              Reloaded, a comprehensive framework guiding researchers in the
              problem-aware selection of metrics. Developed by a large
              international consortium in a multistage Delphi process, it is
              based on the novel concept of a problem fingerprint-a structured
              representation of the given problem that captures all aspects
              that are relevant for metric selection, from the domain interest
              to the properties of the target structure(s), dataset and
              algorithm output. On the basis of the problem fingerprint, users
              are guided through the process of choosing and applying
              appropriate validation metrics while being made aware of
              potential pitfalls. Metrics Reloaded targets image analysis
              problems that can be interpreted as classification tasks at
              image, object or pixel level, namely image-level classification,
              object detection, semantic segmentation and instance segmentation
              tasks. To improve the user experience, we implemented the
              framework in the Metrics Reloaded online tool. Following the
              convergence of ML methodology across application domains, Metrics
              Reloaded fosters the convergence of validation methodology. Its
              applicability is demonstrated for various biomedical use cases.",
  journal  = "Nat. Methods",
  volume   =  21,
  number   =  2,
  pages    = "195--212",
  month    =  feb,
  year     =  2024,
  language = "en"
}

@misc{reinke2023common,
      title={Common Limitations of Image Processing Metrics: A Picture Story}, 
      author={Annika Reinke and Minu D. Tizabi and Carole H. Sudre and Matthias Eisenmann and Tim Rädsch and Michael Baumgartner and Laura Acion and Michela Antonelli and Tal Arbel and Spyridon Bakas and Peter Bankhead and Arriel Benis and Matthew Blaschko and Florian Buettner and M. Jorge Cardoso and Jianxu Chen and Veronika Cheplygina and Evangelia Christodoulou and Beth Cimini and Gary S. Collins and Sandy Engelhardt and Keyvan Farahani and Luciana Ferrer and Adrian Galdran and Bram van Ginneken and Ben Glocker and Patrick Godau and Robert Haase and Fred Hamprecht and Daniel A. Hashimoto and Doreen Heckmann-Nötzel and Peter Hirsch and Michael M. Hoffman and Merel Huisman and Fabian Isensee and Pierre Jannin and Charles E. Kahn and Dagmar Kainmueller and Bernhard Kainz and Alexandros Karargyris and Alan Karthikesalingam and A. Emre Kavur and Hannes Kenngott and Jens Kleesiek and Andreas Kleppe and Sven Kohler and Florian Kofler and Annette Kopp-Schneider and Thijs Kooi and Michal Kozubek and Anna Kreshuk and Tahsin Kurc and Bennett A. Landman and Geert Litjens and Amin Madani and Klaus Maier-Hein and Anne L. Martel and Peter Mattson and Erik Meijering and Bjoern Menze and David Moher and Karel G. M. Moons and Henning Müller and Brennan Nichyporuk and Felix Nickel and M. Alican Noyan and Jens Petersen and Gorkem Polat and Susanne M. Rafelski and Nasir Rajpoot and Mauricio Reyes and Nicola Rieke and Michael Riegler and Hassan Rivaz and Julio Saez-Rodriguez and Clara I. Sánchez and Julien Schroeter and Anindo Saha and M. Alper Selver and Lalith Sharan and Shravya Shetty and Maarten van Smeden and Bram Stieltjes and Ronald M. Summers and Abdel A. Taha and Aleksei Tiulpin and Sotirios A. Tsaftaris and Ben Van Calster and Gaël Varoquaux and Manuel Wiesenfarth and Ziv R. Yaniv and Paul Jäger and Lena Maier-Hein},
      year={2023},
      eprint={2104.05642},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}