标题： 用于从LGE-MRI自动进行多类双心房分割的两阶段nnU-Net 显示英文标题

标题： Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs

摘要： 钆延迟增强磁共振成像（LGE-MRI）用于可视化心房纤维化和瘢痕，为个性化的心房颤动（AF）治疗提供了重要信息。 由于手动分析和这些图像的勾画既费时又可能因主观性而产生变化，我们开发了一种自动流程，用于在LGE-MRI上分割左心房（LA）腔、右心房（RA）腔以及双侧心房壁。 我们的方法基于两阶段nnU-Net架构，结合了2D和3D卷积网络，并引入自适应直方图均衡化以提高输入图像中的组织对比度，以及在输出分割图上的形态学操作。 对于LA、RA和壁，我们分别实现了0.92 ± 0.03、0.93 ± 0.03、0.71 ± 0.05的Dice相似系数和(3.89 ± 6.67) mm、(4.42 ± 1.66) mm、(3.94 ± 1.83) mm的95%豪斯多夫距离。 准确地描绘LA、RA和心肌壁是心血管患者（特别是AF患者）分析心房结构的第一步。 这可以帮助临床医生及时提供适当且个性化的治疗方案。 显示英文摘要

摘要： 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.