| --- |
| license: mit |
| language: |
| - en |
| library_name: pytorch |
| tags: |
| - medical-imaging |
| - coronary-artery-segmentation |
| - cardiac-ct |
| - nnunet |
| - imagecas |
| --- |
| |
| # Coronary Artery Segmentation (nnU-Net) |
|
|
| nnU-Net trained on ImageCAS dataset for coronary artery tree segmentation in cardiac CT angiography (CCTA). |
|
|
| ## Model Details |
|
|
| - **Architecture**: nnU-Net v2 (3D Residual-Encoder, full resolution) |
| - **Input**: 3D CCTA volumes (NIfTI format) |
| - **Output**: Coronary artery voxel-wise segmentation |
|
|
| ## Training |
|
|
| - **Dataset**: ImageCAS (~1,000 CCTA scans) |
| - **Split**: Official ImageCAS split (train/val/test) |
| - **Fold**: fold_0 (best validation performance) |
| |
| ## Downstream Pipeline |
| |
| Part of multi-stage clinical analysis system: |
| 1. **Stage 1** (this model): Coronary segmentation |
| 2. **Stage 2**: Plaque classification (HU-threshold: calcified/non-calcified/LRNC) |
| 3. **Stage 3**: 3D plaque map reconstruction |
| 4. **Stage 4**: FFR estimation (research prototype, CFD-capable) |
| 5. **Stage 5**: Clinical review app with 3D visualization & curved MPR |
| |
| ## Real Case Examples |
| |
| Outputs included demonstrate: |
| - Coronary mesh with plaque composition color-coding (phase4_job_case1_v2) |
| - Curved MPR for interventionalist visualization |
| - Diameter profiling, QC gates, comprehensive reports |
|
|
| ## Citation |
|
|
| ImageCAS: Huo et al., "Towards Robust Coronary Artery Segmentation in CCTA" (2021) |
|
|
| nnU-Net: Isensee et al., "nnU-Net: Self-configuring method for deep learning-based biomedical image segmentation" (2021) |
|
|
| ## License |
|
|
| MIT - See repository for details |
|
|
| ## Disclaimer |
|
|
| Research model. Not for clinical use without independent validation. |
|
|
