---
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.