用心电图和解剖知识辅助心脏核磁，提升瘢痕分割精度。
Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images
- 融合心电图与解剖先验，动态加权时间差异特征。
- 瘢痕分割Dice分数从0.6149提升至0.8463，精准率达0.9115。
- 适合心脏病影像分析、多模态医学图像研究者使用。
从延迟钆增强（LGE）心脏MRI中准确分割心肌瘢痕对评估组织存活率至关重要，但受对比度变化和成像伪影影响仍具挑战。心电图（ECG）信号提供互补生理信息，传导异常可帮助定位或提示瘢痕区域。本文提出一种新型多模态框架，将心电图衍生的电生理信息与AHA-17解剖图谱的先验知识结合，实现生理一致性的LGE基瘢痕分割。由于ECG与LGE-MRI非同步采集，引入时间感知特征融合（TAFF）机制，根据采集时间差动态加权并融合特征。在临床数据集上评估，相比仅使用图像的先进基准（nnU-Net），平均骰指数从0.6149提升至0.8463，精度达0.9115，敏感度为0.9043。结果表明，整合生理与解剖知识使模型“超越图像”，为鲁棒且生理基础坚实的心脏瘢痕分割开辟新方向。
原文摘要 · Abstract (English)
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.
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