Breaking Annotation Dependency in Coronary Stenosis Segmentation via Physics-Driven Sim2Real Learning

MCML Authors

Baochang Zhang

→ Group Nassir Navab
 Computer Aided Medical Procedures & Augmented Reality

Nassir Navab

Prof. Dr.

Core PI

Computer Aided Medical Procedures & Augmented Reality

Abstract

Accurate coronary stenosis segmentation in X-ray angiography (XA) remains fundamentally limited by reliance on scarce and biased manual lesion annotations. Clinical datasets, although realistic, provide only incomplete and skewed sampling of pathological variations, constraining generalization. We address this challenge by reframing stenosis segmentation as a controllable synthetic learning problem and propose a physics-driven sim-to-real framework that eliminates dependence on real lesion labels. We generate a scalable synthetic dataset from patient-specific coronary models reconstructed from CTA, where stenosis variations with diverse severities and morphologies are deliberately designed in 3D space. A physics-based projection process produces geometry-preserving synthetic XA images with precise image-annotation alignment, enabling balanced and distribution-aware supervision beyond biased clinical sampling. A segmentation network trained on this synthetic data is subsequently adapted to real XA through uncertainty-guided self-supervised refinement. Predictive uncertainty is estimated online to select reliable pseudo-labels and drive confidence-aware consistency learning, promoting robust adaptation in ambiguous regions. On the AR-CADE benchmark, our method surpasses existing self-supervised and few-shot approaches and approaches fully supervised performance without using any real stenosis annotations. These results demonstrate that physics-driven sim-to-real learning offers a principled pathway toward annotation-independent coronary lesion analysis.

inproceedings ZDL+26

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MICCAI 2026

29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published.

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Authors

B. Zhang • F. K. Dewi • S. Liu • A. Yousefi • H. Schunkert • R. Ghotbi • N. Navab

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Links

GitHub

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Research Area

 C1 | Medicine