Automatic Epicardial Adipose Tissue Segmentation in Cardiac CT with Position Regularization

Cardiovascular diseases CVDs are a major global health concern. Epicardial adipose tissue EAT has been identified as playing a significant role in the pathogenesis and progression of cardiovascular diseases. While deep learning-based methods have shown promising results in EAT segmentation, they primarily treat EAT as a whole and do not consider the urgent clinical need for fine-grained segmentation at different locations. In this work, we propose a position-aware fine-grained EAT segmentation method that extends existing single-class coarse EAT segmentation to multi-class fine-grained segmentation of RV-, LV-, and PA-EAT. Our method utilizes a two-branch architecture, where one branch specializes in segmentation and the other focuses on precisely positioning centroids of various EATs,thereby enhancing model performance for EAT localization and boosting segmentation accuracy. By leveraging prior knowledge of spatial distributions of different tissues, our method demonstrates favorable performance on a challenging self-collected dataset and a public dataset. The proposed method has the potential to aid in the automatic fine-grained segmentation of EAT, enabling more detailed clinical diagnostic needs.