[2511.14702] Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.14702 (cs) [Submitted on 18 Nov 2025 (v1), last revised 1 Apr 2026 (this version, v4)] Title:Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images Authors:Farheen Ramzan, Yusuf Kiberu, Nikesh Jathanna, Meryem Jabrane, Vicente Grau, Shahnaz Jamil-Copley, Richard H. Clayton, Chen (Cherise)Chen View a PDF of the paper titled Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images, by Farheen Ramzan and 7 other authors View PDF HTML (experimental) Abstract:Accurate segmentation of myocardial scar from late gadolinium enhanced (LGE) cardiac MRI is essential for evaluating tissue viability, yet remains challenging due to variable contrast and imaging artifacts. Electrocardiogram (ECG) signals provide complementary physiological information, as conduction abnormalities can help localize or suggest scarred myocardial regions. In this work, we propose a novel multimodal framework that integrates ECG-derived electrophysiological information with anatomical priors from the AHA-17 atlas for physiologically consistent LGE-based scar segmentation. As ECGs and LGE-MRIs are not acquired simultaneously, we introduce a Temporal Aware Feature Fusion (TAFF) mechanism that dynamically weights and fuses features based on their acquisition time difference. Our method was evaluated on a...