Abstract
Accurate segmentation of the left atrial (LA) wall in 3D late gadolinium-enhanced MRI (LGE-MRI) is essential for wall thickness mapping and fibrosis quantification, yet it remains challenging due to the wall's thinness, complex anatomy, and low contrast. We propose C2W-Tune, a two-stage cavity-to-wall transfer framework that leverages a high-accuracy LA cavity model as an anatomical prior to improve thin-wall delineation. Using a 3D U-Net with a ResNeXt encoder and instance normalization, Stage 1 pre-trains the network to segment the LA cavity, learning robust atrial representations. Stage 2 transfers these weights and adapts the network to LA wall segmentation using a progressive layer-unfreezing schedule to preserve endocardial features while enabling wall-specific refinement. Experiments on the 2018 LA Segmentation Challenge dataset demonstrate substantial gains over an architecture-matched baseline trained from scratch: wall Dice improves from 0.623 to 0.814, and Surface Dice at 1 mm improves from 0.553 to 0.731. Boundary errors were substantially reduced, with the 95th-percentile Hausdorff distance (HD95) decreasing from 2.95 mm to 2.55 mm and the average symmetric surface distance (ASSD) from 0.71 mm to 0.63 mm. Furthermore, even with reduced supervision (70 training volumes sampled from the same training pool), C2W-Tune achieved a Dice score of 0.78 and an HD95 of 3.15 mm, maintaining competitive performance and exceeding multi-class benchmarks that typically report Dice values around 0.6-0.7. These results show that anatomically grounded task transfer with controlled fine-tuning improves boundary accuracy for thin LA wall segmentation in 3D LGE-MRI.
Abstract (translated)
在三维延迟钆增强磁共振成像（LGE-MRI）中，左心房（LA）壁的精确分割对于壁厚映射和纤维化定量至关重要，但由于心房壁薄、解剖结构复杂且图像对比度低，该任务仍具有挑战性。我们提出C2W-Tune，一种两阶段腔壁迁移框架，该框架利用高精度左心房腔体模型作为解剖先验信息以改善薄壁分割。第一阶段采用带有ResNeXt编码器和实例归一化的3D U-Net，预训练网络分割左心房腔体，学习稳健的心房表征。第二阶段迁移这些权重并调整网络以适应左心房壁分割，通过渐进式解冻策略在保留心内膜特征的同时实现壁特异性精细化。在2018年左心房分割挑战数据集上的实验表明，与从头训练的架构匹配基线相比，C2W-Tune取得显著提升：壁部Dice系数从0.623提高到0.814，1毫米表面Dice从0.553提高到0.731。边界误差大幅降低，95百分位Hausdorff距离（HD95）从2.95毫米降至2.55毫米，平均对称表面距离（ASSD）从0.71毫米降至0.63毫米。此外，即使在监督数据减少（从同一训练池中采样70个训练容积）的情况下，C2W-Tune仍达到0.78的Dice系数和3.15毫米的HD95，保持竞争力并超越通常报告Dice值在0.6-0.7之间的多类基准。这些结果表明，基于解剖学依据的任务迁移结合受控微调，可提升三维LGE-MRI中薄左心房壁分割的边界精度。
URL
https://arxiv.org/abs/2603.24992
