[1]王萍,徐凯成,张一弛,等.基于反注意力机制U-Net网络的胃部肿瘤分割[J].中国医学物理学杂志,2022,39(9):1133-1139.[doi:DOI:10.3969/j.issn.1005-202X.2022.09.013]
　WANG Ping,XU Kaicheng,ZHANG Yichi,et al.Gastric tumor segmentation by U-Net based on reverse attention mechanism[J].Chinese Journal of Medical Physics,2022,39(9):1133-1139.[doi:DOI:10.3969/j.issn.1005-202X.2022.09.013]
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基于反注意力机制U-Net网络的胃部肿瘤分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第9期
页码:
1133-1139
栏目:
医学影像物理
出版日期:
2022-11-02

文章信息/Info

Title:
Gastric tumor segmentation by U-Net based on reverse attention mechanism
文章编号:
1005-202X（2022）09-1133-07
作者:
王萍1; 徐凯成2; 张一弛2; 王海玲2; 蔡清萍3; 卫子然3; 胡尊琪3
1.上海工程技术大学继续教育学院， 上海 201620； 2.上海工程技术大学电子电气工程学院， 上海 201620； 3.上海长征医院肠胃外科， 上海 200003
Author(s):
WANG Ping1;  XU Kaicheng2;  ZHANG Yichi2;  WANG Hailing2;  CAI Qingping3;  WEI Ziran3;  HU Zunqi3
1. School of Continuing Education, Shanghai University of Engineering Science, Shanghai 201620, China 2. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 3. Department of Gastrointestinal Surgery, Shanghai Changzheng Hospital, Shanghai 200003, China
关键词:
胃部肿瘤分割; 深度学习; 图像处理; 反注意力机制; U-Net网络
Keywords:
Keywords: gastric tumor segmentation deep learning image processing reverse attention mechanism U-Net
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2022.09.013
文献标志码:
A
摘要:
首先利用全局与局部注意力对肿瘤进行定位，然后在模型中加入反注意力机制，将显著特征从原特征图中消除，并保留肿瘤的边缘轮廓信息。此外还在模型中使用深度监督，监督各个深度解码层的训练，有效抑制模型梯度消失现象，提高分割的准确性。本研究使用的是上海长征医院的胃部CT数据集，并将提出的模型与U-Net、Attention U-Net和ET-Net的实验对比。研究结果表明，相较于传统的U-Net网络模型，基于反注意力机制的U-Net模型在胃部肿瘤分割中性能得到了较大的提高，证明了该网络模型的有效性。
Abstract:
Abstract: The global and local attention mechanisms are used to localize tumor, and a reverse attention mechanism is added to the model to remove the salient features from the original feature map while retaining the edge contour information. In addition, deep supervision is also applied to supervise the training of each deep decoding layer, which effectively suppresses gradient disappearance and enhances segmentation accuracy. The gastric CT data set used in the study is from Shanghai Changzheng Hospital. The performance of U-Net model with reverse attention mechanism in gastric tumor segmentation has been greatly improved when compared with the traditional U-Net networks (U-net, Attention U-net and ET-Net), which proves the effectiveness of the proposed model.

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备注/Memo

备注/Memo:
【收稿日期】2022-04-15 【基金项目】上海市科学技术委员会科研计划项目（18411952800） 【作者简介】王萍，硕士，助理研究员，研究方向：智慧医疗与管理，E-mail: 00150001@sues.edu.cn 【通信作者】王海玲，博士，讲师，研究方向：智慧医疗、人工智能，E-mail: wanghailing@sues.edu.cn
更新日期/Last Update: 2022-09-27