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README.md

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+---
+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.