Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.

中文翻译：

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LA-Net：用于左心房分割的多任务深度网络


尽管心房颤动 (AF) 是最常见的持续性房性心律失常，但这种情况的治疗成功率仍然不理想。来自磁共振成像 (MRI) 的信息有可能提高治疗效果，但目前用于在 MR 图像中分割心房的自动工具很少。在这项研究中，我们提出了 LA-Net，这是一种经过优化的多任务网络，可同时从 MRI 生成左心房分割和边缘掩模。 LA-Net 包括交叉注意模块 (CAM) 和增强型解码器模块 (EDM)，有目的地选择最有意义的边缘信息进行分割，并将其平滑地合并到多尺度的分割掩模中。我们评估了 LA-Net 在两个 MR 序列上的性能：晚期钆增强 (LGE) 心房 MRI 和心房短轴平衡稳态自由进动 (bSSFP) MRI。 LA-Net 在 LGE (STACOM 2018) 数据集上给出的 Hausdorff 距离为 12.43 mm，Dice 得分为 0.92，在 bSSFP（内部）数据集上给出的 Hausdorff 距离为 17.41 mm，Dice 得分为 0.90，无需任何后处理，超越了之前的水平提出的分割网络，包括 U-Net 和 SEGANet。我们的方法可以从 MR 图像中自动提取有关 LA 的信息，这可以在 AF 患者的管理中发挥重要作用。