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 clinical dataset and achieved substantial gains over the state-of-the-art image-only baseline (nnU-Net), increasing the average Dice score for scars from 0.6149 to 0.8463 and achieving high performance in both precision (0.9115) and sensitivity (0.9043). These results show that integrating physiological and anatomical knowledge allows the model to "see beyond the image", setting a new direction for robust and physiologically grounded cardiac scar segmentation.

翻译：从晚期钇增强（LGE）心脏磁共振成像中准确分割心肌瘢痕对于评估组织活性至关重要，但由于对比度多变和成像伪影的存在，这仍然是一项挑战。心电图（ECG）信号提供了互补的生理学信息，因为传导异常有助于定位或提示心肌瘢痕区域。在本研究中，我们提出了一种新颖的多模态框架，该框架将ECG衍生的电生理信息与来自AHA-17图谱的解剖学先验知识相结合，以实现生理学上一致的、基于LGE的瘢痕分割。鉴于ECG和LGE-MRI并非同时采集，我们引入了一种时序感知特征融合（TAFF）机制，该机制根据采集时间差动态加权和融合特征。我们的方法在临床数据集上进行了评估，相较于最先进的纯图像基线方法（nnU-Net），取得了显著提升：瘢痕的平均Dice分数从0.6149提高到0.8463，并且在精确度（0.9115）和灵敏度（0.9043）方面均表现出色。这些结果表明，整合生理学和解剖学知识使模型能够“超越图像所见”，为稳健且基于生理学的心脏瘢痕分割指明了新方向。