MEDU-Net+: a novel improved U-Net based on multi-scale encoder-decoder for medical image segmentation

Abstract

The unique U-shaped structure of U-Net network makes it achieve good performance in image segmentation.This network is a lightweight network with a small number of parameters for small image segmentation datasets.However, when the medical image to be segmented contains a lot of detailed information, the segmentation results cannot fully meet the actual requirements.In order to achieve higher accuracy of medical image segmentation, a novel improved U-Net network architecture called multi-scale encoder-decoder U-Net+ (MEDU-Net+) is proposed in this paper.We design the GoogLeNet for achieving more information at the encoder of the proposed MEDU-Net+, and present the multi-scale feature extraction for fusing semantic information of different scales in the encoder and decoder.Meanwhile, we also introduce the layer-by-layer skip connection to connect the information of each layer, so that there is no need to encode the last layer and return the information.The proposed MEDU-Net+ divides the unknown depth network into each part of deconvolution layer to replace the direct connection of the encoder and decoder in U-Net.In addition, a new combined loss function is proposed to extract more edge information by combining the advantages of the generalized dice and the focal loss functions.Finally, we validate our proposed MEDU-Net+ and other classic medical image segmentation networks on three medical image datasets.The experimental results show that our proposed MEDU-Net+ has prominent superior performance compared with other medical image segmentation networks.