基于课程学习的室壁瘢痕分割用于缺血性和非缺血性心肌病
Curriculum-Guided Myocardial Scar Segmentation for Ischemic and Non-ischemic Cardiomyopathy
AI总结：
本文提出一种基于课程学习的框架，通过渐进训练策略提升LGE-CMR图像中室壁瘢痕分割的性能，尤其在低置信度或视觉模糊样本中表现更优，提升了瘢痕量化的一致性和准确性。
摘要：
识别和量化心肌瘢痕对心血管疾病的诊断和预后至关重要。然而，由于患者间对比增强的差异、次优的成像条件如对比剂排泄以及由观察者差异引起的弥漫性瘢痕标注不一致，从晚期钆增强心脏磁共振（LGE-CMR）图像中可靠地分割瘢痕仍具挑战性。在本文中，我们提出了一种基于课程学习的框架，旨在在这些具有挑战性的条件下提高分割性能。该方法引入了一种渐进训练策略，将模型从高置信度、清晰定义的瘢痕区域引导到低置信度或视觉模糊的样本，这些样本具有有限的瘢痕负担。通过以这种方式结构化学习过程，网络能够发展出对不确定标签和细微瘢痕外观的鲁棒性，这些特征在传统训练管道中往往被低估。实验结果表明，所提出的方法提高了分割的准确性和一致性，特别是在瘢痕较少或弥漫性瘢痕的情况下，优于标准训练基线。该策略为利用不完美数据改进临床应用中的心肌瘢痕量化提供了一种系统方法。我们的代码已公开在GitHub上。
英文摘要：
Identification and quantification of myocardial scar is important for diagnosis and prognosis of cardiovascular diseases. However, reliable scar segmentation from Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR) images remains a challenge due to variations in contrast enhancement across patients, suboptimal imaging conditions such as post contrast washout, and inconsistencies in ground truth annotations on diffuse scars caused by inter observer variability. In this work, we propose a curriculum learning-based framework designed to improve segmentation performance under these challenging conditions. The method introduces a progressive training strategy that guides the model from high-confidence, clearly defined scar regions to low confidence or visually ambiguous samples with limited scar burden. By structuring the learning process in this manner, the network develops robustness to uncertain labels and subtle scar appearances that are often underrepresented in conventional training pipelines. Experimental results show that the proposed approach enhances segmentation accuracy and consistency, particularly for cases with minimal or diffuse scar, outperforming standard training baselines. This strategy provides a principled way to leverage imperfect data for improved myocardial scar quantification in clinical applications. Our code is publicly available on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
