计算机应用研究2024，Vol.41Issue(4)：1264-1269,6.DOI:10.19734/j.issn.1001-3695.2023.07.0352
基于卷积胶囊编码器和多尺度局部特征共现的图像分割网络
Medical image segmentation network based on convolution capsule encoder and multi-scale local feature co-occurrence
摘要
Abstract
U-Net has achieved great success in the field of image segmentation.However,some of the position information is lost in the process of convolution and downsampling,model is difficult to learn global and long-range semantic interaction infor-mation and lacks the ability to integrate global and local information.To extract rich local detail and contextual information,this paper proposed an image segmentation network called MLFCNet,combining a convolutional module and a capsule en-coder.Based on the U-Net,this paper introduced a capsule network module to learn target positional information and the rela-tionships between local and global information.At the same time,the proposed attention mechanism could retain the informa-tion discarded by the network pooling layer.This paper designed a new attention mechanism so that multi-scale features could be fused,where global information was captured and background noise was suppressed.In addition,it proposed a new local feature co-occurrence algorithm to better learn the relationship between local features.The proposed method was compared with nine methods on two public datasets,mIoU improves 4.7％and Dice coefficient improves 1.7％in liver medical images com-pared to the second highest performing model.Experimental results on the dataset of liver and dataset of human show that un-der the same experimental conditions,the proposed network is superior to U-Net and other mainstream image segmentation net-works.关键词
U-Net/卷积胶囊编码器/注意力机制/多尺度特征局部共现Key words
U-Net/convolutional capsule encoder/attention mechanism/multi-scale local feature co-occurrence分类
信息技术与安全科学引用本文复制引用
秦辰栋,王永雄,张佳鹏..基于卷积胶囊编码器和多尺度局部特征共现的图像分割网络[J].计算机应用研究,2024,41(4):1264-1269,6.基金项目
上海市自然科学基金资助项目(22ZR1443700) （22ZR1443700）
