北京理工大学 增强现实智能医疗实验室

Abstract—The accurate 3D left ventricular (LV) myocardium segmentation in short-axis (SAX) view of cardiac magnetic resonance (CMR) is challenged by the sparse spatial structure of CMR. The strategy of multi-view CMR fusion can provide fine-grained spatial structure for accurate segmentation. However, the large information misalignment and lack of dense 3D CMR as fusion target in multi-view CMR fusion, and the different spatial resolution between the fusion result and the ground truth in segmentation limit the strategy. In this study, we propose a multi-view spatial-aware adversarial network (MVSGAN). It studies the perception of fine-grained cardiac structure for accurate segmentation by the spatialaware multi-view CMR fusion. It consists of three modules: (1) A residual adversarial fusion (RAF) module takes inter-slices deep correlation and anatomical prior to refine the spatial structures by residual supplement and adversarial optimization. (2) A structural perception-aggregation (SPA) module establishes the spatial correlation between the dense cardiac model and sparse label for accurate CMR LV myocardium segmentation. (3) A joint training strategy utilizes the dense SAX volume as explicit and implicit goals to jointly optimize the framework. The experiments are applied on a public dataset and a clinical dataset to evaluate the performance of MVSGAN. The average Dice and Jaccard score of LV myocardium segmentation obtained by MVSGAN are highest among seven existing state-of-the-art methods, which are up to 0.92 and 0.75. It is concluded that the spatial-aware multi-view CMR fusion can provide meaningful spatial correlation for accurate LV myocardium segmentation.