基于ResNext的框架用于双心房结构分割
Segmenting Bi-Atrial Structures Using ResNext Based Framework
AI总结：
针对房颤诊疗中双心房手动分割效率低、差异大的问题，提出基于ResNeXt编码器和循环学习率的两阶段框架TASSNet，可从3D LGE-MRI中高精度全自动分割双心房结构，适用于分布外数据，助力临床房颤管理。
摘要：
心房颤动（Atrial Fibrillation, AF）是全球最常见的持续性心律失常，目前越来越需要精准的双心房结构评估来指导消融策略，尤其是在持续性房颤中。晚期钆增强磁共振成像（Late gadolinium-enhanced magnetic resonance imaging, LGE-MRI）可实现心房纤维化的可视化，但精准的手动分割仍耗时久、依赖操作者，且易出现结果差异。我们提出TASSNet，这是一种新型两阶段深度学习框架，可从3D LGE-MRI中全自动分割左心房（left atrium, LA）和右心房（right atrium, RA），包括心房壁和心房腔。TASSNet有两大创新：（i）基于ResNeXt的编码器，用于增强有限医学数据集下的特征提取；（ii）循环学习率调度策略，用于解决高度不平衡、小批量3D分割任务中的收敛不稳定问题。我们在两个数据集上评估了该方法，其中一个是完全分布外的数据集，未进行任何额外训练。在两种情况下，TASSNet都能高精度地成功分割心房结构。这些结果凸显了TASSNet在实现稳健且可重复的双心房分割方面的潜力，可为临床房颤管理中的高级纤维化量化和个性化消融规划提供支持。
英文摘要：
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.
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。
