MAAUNet:Exploration of U-shaped encoding and decoding structure for semantic segmentation of medical image  

MAAUNet: 医学图像语义分割U型编解码结构探索

作　　者：SHAO Shuo GE Hongwei 邵硕;葛洪伟(江南大学江苏省模式识别与计算智能实验室,江苏无锡214122;江南大学人工智能与计算机学院,江苏无锡214122)

机构地区：[1]Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,Jiangnan University,Wuxi 214122,China [2]School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China

出　　处：《Journal of Measurement Science and Instrumentation》2022年第4期418-429,共12页测试科学与仪器（英文版）

基　　金：National Natural Science Foundation of China(No.61806006);Jiangsu University Superior Discipline Construction Project。

摘　　要：In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.

关 键 词：U-shaped attention network structure of MAAUNet convolutional neural network encoding-decoding structure attention mechanism medical image semantic segmentation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R-05[自动化与计算机技术—计算机科学与技术]