Physics-informed Model Selection for Robust Deep Learning Segmentation of Multi-center Perfusion CMR: Initial Findings from the SCMR Registry

Motivation: Accurate segmentation of first-pass perfusion CMR is critical for reliable myocardial blood flow analysis. Goal(s): To enable generalizaton to multi-center datasets. Approach: Incorporating cardiac magnetic resonance physics-informed features in the deep learning model selection process. Results: Our results, which leverage multi-center perfusion CMR datasets from the SCMR registry, suggest that combining physics-informed guidance with uncertainty-based deep learning model selection improves the segmentation performance in deep learning-based analysis of multi-center stress FPP datasets. Impact: The proposed hybrid approach has the potential to improve the reliability of fully automated stress/rest FPP analysis in clinical settings and in multi-center clinical trials.