论文标题
对病理分割的基于解剖学先验的U-NET注意
Anatomy Prior Based U-net for Pathology Segmentation with Attention
论文作者
论文摘要
心脏磁共振（MR）图像中的病理区域分割在心血管疾病的临床诊断中起着至关重要的作用。由于形状不规则和小区域，病理分割一直是一项具有挑战性的任务。我们提出了一个基于解剖的基于解剖的框架，该框架将U-NET分割网络与注意技术结合在一起。利用病理是包容性的事实，我们提出了一种邻里罚款策略，以评估心肌与心肌梗死和无流量区域之间的包容关系。该社区罚款策略可以应用于具有包容关系（例如整个梗塞和心肌等）的任何两个标签，以形成邻近的损失。提出的框架在EMIDEC数据集上进行了评估。结果表明，我们的框架在病理区域细分中有效。
Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases. Because of the irregular shape and small area, pathological segmentation has always been a challenging task. We propose an anatomy prior based framework, which combines the U-net segmentation network with the attention technique. Leveraging the fact that the pathology is inclusive, we propose a neighborhood penalty strategy to gauge the inclusion relationship between the myocardium and the myocardial infarction and no-reflow areas. This neighborhood penalty strategy can be applied to any two labels with inclusive relationships (such as the whole infarction and myocardium, etc.) to form a neighboring loss. The proposed framework is evaluated on the EMIDEC dataset. Results show that our framework is effective in pathological area segmentation.
