论文标题
Atrialjsqnet：一个新框架，用于左心房的联合分割和量化，并结合了空间和形状信息
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
论文作者
论文摘要
从晚期增强的磁共振成像（LGE MRI）的左心房（LA）和心房疤痕分割是临床实践中的重要任务。 ％，指导消融疗法并预测房颤（AF）患者的治疗结果。然而，由于图像质量差，各种LA形状，薄壁和周围的增强区域，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，并忽略了洛杉矶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Artialjsqnet，其中LA分割，疤痕投影到LA表面，并以端到端的样式同时执行疤痕定量。我们通过明确的表面投影提出了形状注意力（SA）的机制，以利用LA和LA疤痕之间的固有相关性。在特定的情况下，SA方案嵌入了多任务结构中，以执行关节分割和疤痕量化。此外，引入了空间编码（SE）损耗以结合目标的连续空间信息，以减少预测分割中的嘈杂斑块。我们评估了MICCAI2018 LA Challenge的60 LGE MRI的拟议框架。在公共数据集上进行的广泛实验证明了拟议的Atrialjsqnet的影响，该实验在最先进的情况下实现了竞争性能。明确探索了LA分割和疤痕量化之间的相关性，并显示了两项任务的显着性能提高。一旦手稿通过https://zmiclab.github.io/projects.html接受，该代码和结果将公开发布。
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. %, to guide ablation therapy and predict treatment results for atrial fibrillation (AF) patients. 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 explicit surface projection, to utilize the inherent correlation between LA and LA 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 LGE MRIs from the MICCAI2018 LA challenge. Extensive experiments on a public dataset 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 and results will be released publicly once the manuscript is accepted for publication via https://zmiclab.github.io/projects.html.
