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Step by Step Instruction

Data

The data is provided in /sample_data, which include the patient chest CT scan. AirwayNet processes these CT scans as input to generate binary airway models and hierarchical anatomical labels.

AirwayNet Process

The dicated pipeline wrapper in airwayatlas_pipeline.py, simply execute the following command:

python airwayatlas_pipeline.py

You will see similar log messages as follows:

(INFO) AirwayNet: Binary Airway initialized.
(INFO) AirwayNet: Multi-class Airway Anatomy initialized. 
(INFO) AirwayNet: Lobe extraction complete.
(INFO) AirwayNet: Airway modeling complete.
(INFO) AirwayNet: Skeleton computation complete.
(INFO) AirwayNet: Saving complete.
(INFO) AirwayNet: Finished tree-parsing.
(INFO) AirwayNet: Finished feature extraction.
(INFO) AirwayNet: Finished inference dataset building.
(INFO) AirwayNet: Finished prediction.

Data input and output structure

Data input structure:

project_root/
├── sample_data/              # Input directory containing patient chest CT scans
│   ├── patient_01/
│   │   ├── image.nii.gz      # CT scan file

After the AirwayNet processing is complete, the main results are, by default, saved in the same directory as the input data:

project_root/
├── sample_data/              # Input directory containing patient chest CT scans
│   ├── patient_01/
│   │   ├── image.nii.gz                             # CT scan file
│   │   ├── airway_bin.nii.gz                        # binary airway structure
│   │   ├── patient_01_pred_lob.nii.gz               # lobar airway anatomy
│   │   ├── patient_01_pred_seg.nii.gz               # segmental airway anatomy
│   │   ├── patient_01_pred_sub.nii.gz               # subsegmental airway anatomy

Meanwhile, the efficient Branching Pattern Analysis can be found in branchingpattern/airwaybranchpattern_pipeline.py. The morphological airway signatures can be found in features/airway_morph_features.py. These are described in detail in the Method section with accompanying pseudocode.

Expected run time

AirwayNet was deployed and tested on a system with the following hardware configuration: a 12th Gen Intel® Core™ i9-12900KF CPU, 64 GB of system memory, and an NVIDIA RTX 3090 GPU with 24 GB of VRAM.

The pipeline processes relatively large 3D CT scans (with a typical volume size of approximately 700×512×512) as input and requires about 5–7 minutes per case to complete the entire process.