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plaque-analysis – AI Model by vasandesh | AlphaNeural AI
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vasandesh
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plaque-analysis
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pytorch
medical-imaging
coronary-artery-segmentation
cardiac-ct
nnunet
imagecas
en
mit
us
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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:
Stage 1
(this model): Coronary segmentation
Stage 2
: Plaque classification (HU-threshold: calcified/non-calcified/LRNC)
Stage 3
: 3D plaque map reconstruction
Stage 4
: FFR estimation (research prototype, CFD-capable)
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.
