Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation

Abstract

Deep learning models, especially U-Net and its derivate models, have been widely used in medical image segmentation. These approaches have achieved promising results in many medical image segmentation tasks with a limited number of training samples. We aim on enhancing medical image segmentation by using spatial continuity information in a proposed Multi-Encoder Parse-Decoder Network (MEPDNet) based on the fact that most of the medical images are sampled continuously. Sequential images are input into parameter shared encoders for getting feature maps, which are then fused by a fusion block. A V$\Lambda$-block is structured to parse the fused feature map to extract the hidden continuity information. The reconstructed feature map is fed into a decoder for generating segmentation masks. Experiments on three datasets show MEPDNet outperforms other state-of-the-art segmentation models while using the least parameters.