Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. The automatic segmentation is however still challenging due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an implicit surface projection to utilize the inherent correlation between LA cavity and scars. In specific, the SA scheme is embedded into a multi-task architecture to perform joint LA segmentation and scar quantification. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target in order to reduce noisy patches in the predicted segmentation. We evaluated the proposed framework on 60 post-ablation LGE MRIs from the MICCAI2018 Atrial Segmentation Challenge. Moreover, we explored the domain generalization ability of the proposed AtrialJSQnet on 40 pre-ablation LGE MRIs from this challenge and 30 post-ablation multi-center LGE MRIs from another challenge (ISBI2012 Left Atrium Fibrosis and Scar Segmentation Challenge). Extensive experiments on public datasets demonstrated the effect of the proposed AtrialJSQnet, which achieved competitive performance over the state-of-the-art. The relatedness between LA segmentation and scar quantification was explicitly explored and has shown significant performance improvements for both tasks. The code has been released via https://zmiclab.github.io/projects.html.

晚期钆增强磁共振成像 (LGE MRI) 的左心房 (LA) 和心房疤痕分割是临床实践中的一项重要任务。然而，由于图像质量差、LA形状多样、壁薄以及周围的增强区域，自动分割仍然具有挑战性。以前的方法通常独立解决这两个任务，而忽略了 LA 和疤痕之间的内在空间关系。在这项工作中，我们开发了一个新的框架，即 ATrialJSQnet，其中 LA 分割、疤痕投影到 LA 表面以及疤痕量化以端到端的方式同时进行。我们提出了一种通过隐式表面投影的形状注意（SA）机制，以利用 LA 腔和疤痕之间的内在相关性。具体来说，SA方案被嵌入到多任务架构中以执行联合LA分割和疤痕量化。此外，引入空间编码（SE）损失来合并目标的连续空间信息，以减少预测分割中的噪声块。我们在MICCAI2018 心房分割挑战赛中的 60 个消融后 LGE MRI 上评估了所提出的框架。此外，我们还探讨了所提出的 AtrialJSQnet 在本次挑战赛中的 40 个消融前 LGE MRI 和另一个挑战赛（ ISBI2012 左心房纤维化和疤痕分割挑战赛）中的 30 个消融后多中心 LGE MRI 上的域泛化能力。对公共数据集的大量实验证明了所提出的 ATrialJSQnet 的效果，它取得了超过最先进技术的竞争性能。LA 分割和疤痕量化之间的相关性得到了明确的探索，并显示出这两项任务的显着性能改进。该代码已通过 https://zmiclab.github.io/projects.html 发布。