自动分割心脏核磁影像中左右心房及心肌壁，提升房颤诊疗效率。
Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs
- 两阶段nnU-Net融合2D与3D网络，增强图像对比度并优化分割结果。
- 左/右心房分割Dice达0.92~0.93，心肌壁分割准确率71%，误差小于4毫米。
- 适用于房颤患者精准治疗规划，适合心血管影像研究与临床应用。
晚期钆增强磁共振成像（LGE-MRI）用于可视化心房纤维化和瘢痕，为个性化房颤（AF）治疗提供关键信息。由于人工分析耗时且存在主观差异，本文开发了一套自动分割方法，对LGE-MRI中的左心房（LA）腔、右心房（RA）腔及双侧心房壁进行分割。方法基于两阶段nnU-Net架构，结合2D与3D卷积网络，并引入自适应直方图均衡化提升输入图像组织对比度，以及形态学操作优化输出分割图。实验结果显示，LA、RA和心房壁的平均Dice相似系数分别为0.92 ± 0.03、0.93 ± 0.03、0.71 ± 0.05，95%豪斯多夫距离分别为(3.89 ± 6.67) mm、(4.42 ± 1.66) mm、(3.94 ± 1.83) mm。精确勾画心房结构是心血管疾病尤其是房颤患者分析的第一步，有助于临床快速制定个体化治疗方案。
原文摘要 · Abstract (English)
Late gadolinium enhancement magnetic resonance imaging (LGE-MRI) is used to visualise atrial fibrosis and scars, providing important information for personalised atrial fibrillation (AF) treatments. Since manual analysis and delineations of these images can be both labour-intensive and subject to variability, we develop an automatic pipeline to perform segmentation of the left atrial (LA) cavity, the right atrial (RA) cavity, and the wall of both atria on LGE-MRI. Our method is based on a two-stage nnU-Net architecture, combining 2D and 3D convolutional networks, and incorporates adaptive histogram equalisation to improve tissue contrast in the input images and morphological operations on the output segmentation maps. We achieve Dice similarity coefficients of 0.92 +/- 0.03, 0.93 +/- 0.03, 0.71 +/- 0.05 and 95% Hausdorff distances of (3.89 +/- 6.67) mm, (4.42 +/- 1.66) mm and (3.94 +/- 1.83) mm for LA, RA, and wall, respectively. The accurate delineation of the LA, RA and the myocardial wall is the first step in analysing atrial structure in cardiovascular patients, especially those with AF. This can allow clinicians to provide adequate and personalised treatment plans in a timely manner.
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