This repository contains the PyTorch implementation of "Automatic Calcium Scoring in Coronary CT Angiography using Deep Learning: Automatically Derived using Spectral CT and Validated using Multiple CTA Imaging Protocols"
- PyTorch 1.7
- SimpleITK
- numpy
To train and predict on CTA cases, organize the data according to the following format:
data
├── Images
| ├── case1_iso.nii.gz
| └── case2_iso.nii.gz
├── Labels
| ├── case1_cal_seg.nii.gz
| ├── case1_cal_map.nii.gz
| ├── case2_cal_seg.nii.gz
| └── case2_cal_map.nii.gz
├── train_ID_lis.txt
└── val_ID_lis.txt
where caseID_cal_seg.nii.gz is the calcification segmentation and caseID_cal_map.nii.gz is the CAC score distributution according to voxel-wise calcification severity.
To train the segmentation model, run:
python train_seg.py
To train the CAC score regression model, run:
python train_regress.py
To run testing on unseen data, first generate the segmentation results by running:
predict_segmentation.py
To run the regression model, run:
predict_regress.py
Note that the segmentation results must be made available before running the regression model.
