Papers › Multi-encoder parse-decoder network for sequential medical image segmentation
Multi-encoder parse-decoder network for sequential medical image segmentation
Dachuan Shi, Ruiyang Liu, Linmi Tao, Zuoxiang He, Li Huo
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 VΛ-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.
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