用ResNeXt框架实现心房自动分割，提升房颤治疗精准度。
Segmenting Bi-Atrial Structures Using ResNext Based Framework
- 基于ResNeXt的编码器增强小样本医学图像特征提取
- 在两个数据集上实现高精度心房结构分割，包括左右心房壁和腔体
- 适合需要个性化消融治疗规划的临床医生和心脏影像研究者
房颤（AF）是全球最常见的持续性心律失常，尤其在持续性房颤中，精确的双心房结构评估对指导消融策略至关重要。晚期钆增强磁共振成像（LGE-MRI）可可视化心房纤维化，但手动分割耗时、依赖操作者且结果易变。本文提出TASSNet，一种用于3D LGE-MRI中左心房（LA）和右心房（RA）全自动分割的两阶段深度学习框架，包括心房壁和腔体。TASSNet引入两大创新：(i) 基于ResNeXt的编码器，增强从有限医学数据中提取特征的能力；(ii) 循环学习率调度，缓解高不平衡、小批量3D分割任务中的收敛不稳定性。我们在两个数据集上评估该方法，其中一个为完全分布外数据，未进行额外训练。结果表明，TASSNet在两种情况下均成功实现高精度心房结构分割。这些结果凸显了TASSNet在鲁棒、可重复的双心房分割中的潜力，有助于实现先进的纤维化量化与个性化消融规划，推动临床房颤管理。
原文摘要 · Abstract (English)
Atrial Fibrillation (AF), the most common sustained cardiac arrhythmia worldwide, increasingly requires accurate bi-atrial structural assessment to guide ablation strategies, particularly in persistent AF. Late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) enables visualisation of atrial fibrosis, but precise manual segmentation remains time-consuming, operator-dependent, and prone to variability. We propose TASSNet, a novel two-stage deep learning framework for fully automated segmentation of both left atrium (LA) and right atrium (RA), including atrial walls and cavities, from 3D LGE-MRI. TASSNet introduces two main innovations: (i) a ResNeXt-based encoder to enhance feature extraction from limited medical datasets, and (ii) a cyclical learning rate schedule to address convergence instability in highly imbalanced, small-batch 3D segmentation tasks. We evaluated our method on two datasets, one of which was completely out-of-distribution, without any additional training. In both cases, TASSNet successfully segmented atrial structures with high accuracy. These results highlight TASSNet's potential for robust and reproducible bi-atrial segmentation, enabling advanced fibrosis quantification and personalised ablation planning in clinical AF management.
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