论文标题
多深度边界感知左心房疤痕细分网络
Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network
论文作者
论文摘要
从晚期增强CMR图像的左心（LA）疤痕的自动分割是房颤（AF）复发分析的关键步骤。然而，由于疤痕形状的变化，描绘的LA疤痕是繁琐的，容易出错的。在这项工作中，我们提出了一个边界感知的LA疤痕分割网络，该网络分别由两个分支来分割LA和LA SCARS。我们探讨了洛杉矶和洛杉矶疤痕之间固有的空间关系。通过在两个分割分支之间引入SOBEL融合模块，可以将LA边界的空间信息从LA分支传播到疤痕分支。因此，可以在LA边界区域进行LA疤痕分割。在我们的实验中，使用了40张标记的图像来训练所提出的网络，其余20个标记的图像用于评估。该网络的平均骰子得分为0.608，用于LA疤痕分段。
Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to segment LA and LA scars, respectively. We explore the inherent spatial relationship between LA and LA scars. By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propagated from the LA branch to the scar branch. Thus, LA scar segmentation can be performed condition on the LA boundaries regions. In our experiments, 40 labeled images were used to train the proposed network, and the remaining 20 labeled images were used for evaluation. The network achieved an average Dice score of 0.608 for LA scar segmentation.
