Cardiac MR Image Sequence Segmentation with Temporal Motion Encoding

Pengxiang Wu, Qiaoying Huang, Jingru Yi, Hui Qu, Meng Ye, Leon Axel, Dimitris N. Metaxas

Abstract: The segmentation of cardiac magnetic resonance (MR) images is a critical step for the accurate assessment of cardiac function and the diagnosis of cardiovascular diseases. In this work, we propose a novel segmentation method that is able to effectively leverage the temporal information in cardiac MR image sequences. Specifically, we construct a Temporal Aggregation Module (TAM) to incorporate the temporal image-based features into a backbone spatial segmentation network (such as a 2D U-Net) with negligible extra computation cost. In addition, we also introduce a novel Motion Encoding Module (MEM) to explicitly encode the motion features of the heart. Experimental results demonstrate that each of the two modules enables clear improvements upon the base spatial network, and their combination leads to further enhanced performance. The proposed method outperforms the previous methods significantly, demonstrating the effectiveness of our design.

Keywords: Cardiac MRI, Left ventricle segmentation, Temporal, Motion