Deep learning based project made to automatically mask fetal brains.
This model was trained with a dataset comprising of ~700 reviewed stacks of images and masks, adding to a total of ~20,000 images.
It has a wider range of data to improve its robustness. For the sake of identifying this model against priors, its called emerald.
Here are some images showcasing its performance against the previous model, where the
pl-emerald is available as a ChRIS plugin. You can either run it using the ChRIS cloud GUI
or locally on the command line. To use it locally, move input NIFTI files to a directory and then run
apptainer run docker://ghcr.io/fnndsc/pl-emerald:latest emerald input/ output/To create masks next to the original file, with the names *_mask.nii:
apptainer run docker://ghcr.io/fnndsc/pl-emerald:latest emerald --mask-suffix _mask.nii input/ input/To extract brains without keeping the mask file:
apptainer run docker://ghcr.io/fnndsc/pl-emerald:latest emerald --mask-suffix '' --outputs '0:.nii' input/ output/To create output masks, extracted brains, and masks overlayed on the original with dimmed background (for convenient visualization):
apptainer run docker://ghcr.io/fnndsc/pl-emerald:latest emerald --mask-suffix '_mask.nii' --outputs '0.0:_brain.nii,0.2:_overlay02.nii' input/ output/- Unet can currently only work with 256x256 images
pl-emerald works perfectly fine on the CPU, however it is hard-coded to use GPUs in ChRIS.
On a high-end machine, processing 5 input files takes ~17s on CPU, ~6s on GPU. This includes the boot time of the program (loading models) however it does not include the time it takes to pull and/or process the container image. The TensorFlow base image is 437.67 MB in size, or 2.67 GB in size with Nvidia drivers.
On lower-end hardware, it can take almost 300s to process a single input file using CPU.