Rethinking the Encoder-decoder Structure in Medical Image Segmentation from Releasing Decoder Structure  

作　　者：Jiajia Ni Wei Mu An Pan Zhengming Chen 

机构地区：[1]School of Artificial Intelligence,Anhui Polytechnic University,Wuhu,241000,China [2]School of Information Science and Engineering,HoHai University,Changzhou,213000,China

出　　处：《Journal of Bionic Engineering》2024年第3期1511-1521,共11页仿生工程学报（英文版）

基　　金：funded by the National Key Research and Development Program of China(Grant 2020YFB1708900);the Fundamental Research Funds for the Central Universities(Grant No.B220201044).

摘　　要：Medical image segmentation has witnessed rapid advancements with the emergence of encoder-decoder based methods.In the encoder-decoder structure,the primary goal of the decoding phase is not only to restore feature map resolution,but also to mitigate the loss of feature information incurred during the encoding phase.However,this approach gives rise to a challenge:multiple up-sampling operations in the decoder segment result in the loss of feature information.To address this challenge,we propose a novel network that removes the decoding structure to reduce feature information loss(CBL-Net).In particular,we introduce a Parallel Pooling Module(PPM)to counteract the feature information loss stemming from conventional and pooling operations during the encoding stage.Furthermore,we incorporate a Multiplexed Dilation Convolution(MDC)module to expand the network's receptive field.Also,although we have removed the decoding stage,we still need to recover the feature map resolution.Therefore,we introduced the Global Feature Recovery(GFR)module.It uses attention mechanism for the image feature map resolution recovery,which can effectively reduce the loss of feature information.We conduct extensive experimental evaluations on three publicly available medical image segmentation datasets:DRIVE,CHASEDB and MoNuSeg datasets.Experimental results show that our proposed network outperforms state-of-the-art methods in medical image segmentation.In addition,it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component.

关 键 词：Medical image segmentation Encoder-decoder architecture Attention mechanisms Releasing decoder architecture Neural network 

分 类 号：Q81[生物学—生物工程] TP39[自动化与计算机技术—计算机应用技术]