[1]赵呈陆,方志军,高永彬,等.基于改进型V-net卷积神经网络的胃壁分割方法[J].中国医学物理学杂志,2021,38(10):1243-1250.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.011]
　ZHAO Chenglu,FANG Zhijun,GAO Yongbin,et al.Gastric wall segmentation based on improved V-net convolutional neural network[J].Chinese Journal of Medical Physics,2021,38(10):1243-1250.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.011]
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基于改进型V-net卷积神经网络的胃壁分割方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第10期
页码:
1243-1250
栏目:
医学影像物理
出版日期:
2021-10-27

文章信息/Info

Title:
Gastric wall segmentation based on improved V-net convolutional neural network
文章编号:
1005-202X（2021）10-1243-08
作者:
赵呈陆1; 方志军1; 高永彬1; 王海玲1; 卫子然2; 蔡清萍2
1.上海工程技术大学电子电气工程学院， 上海 201620； 2.上海长征医院普外二科， 上海 200003
Author(s):
ZHAO Chenglu1;  FANG Zhijun1;  GAO Yongbin1;  WANG Hailing1;  WEI Ziran2;  CAI Qingping2
1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. the Second Department of General Surgery, Shanghai Changzheng Hospital, Shanghai 200003, China
关键词:
胃癌; CT图像; 图像分析; 卷积神经网络; 胃壁分割
Keywords:
Keywords: gastric cancer CT image image analysis convolutional neural network gastric wall segmentation
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2021.10.011
文献标志码:
A
摘要:
实现上腹部CT影像的胃壁分割与中心线提取是成功实现早期胃癌筛查和辅助T分期的前提。基于改进型V-net的胃壁分割方法加入了全局平均权重模块的全卷积神经网络框架，有效解决了神经网路下采样过程中信息丢失的问题。此外，本文在原水平集方法的基础上，提出了正则化水平集损失函数。该损失函数有效抑制了全卷积网络胃壁边缘特征丢失率和因数据量较少而引起的过拟合问题，提高了神经网络对上腹部CT影像中胃壁的识别精度。实验表明，在上腹部CT影像数据集中本文方法分割准确度Dice系数高达0.916 5，IOU达到了0.822 3。该方法的Dice相对于3D V-net方法准确度提高了近6%，同时比CE-net和Dense U-net方法的准确率分别提高了2.7%和3.1%。
Abstract:
Abstract: The realization of gastric wall segmentation and centerline extraction in upper abdominal CT images is the premise of successful implementation of early gastric cancer screening and assisting in T staging. Gastric wall segmentation method based on improved V-net which uses the fully convolutional network framework with global average weight module can effectively solve the problem of information loss in neural network subsampling. In addition, a regularized level set loss function is proposed based on the original level set method for effectively suppressing the loss rate of gastric wall edge features and the over-fitting caused by the small amount of data, thereby improving the recognition accuracy of the gastric wall in upper abdominal CT images using neural network. The experiment shows that the Dice coefficient of the proposed method for gastric wall segmentation in upper abdominal CT image data set is up to 0.916 5, and IOU reaches 0.822 3. The segmentation accuracy of the proposed method is improved by 6% compared with 3D-Vnet method, and that is improved by 2.7% and 3.1% compared with CE-net method and Dense U-net method, respectively.

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备注/Memo

备注/Memo:
【收稿日期】2021-06-25 【基金项目】上海市科委重点项目(18411952800) 【作者简介】赵呈陆，硕士研究生在读，研究方向：计算机视觉、智慧医疗，E-mail: 18252100195@163.com；方志军，博士研究生，教授、博士生导师、IEEE/ACM高级会员，主要从事模式识别、智能计算、视频分析等方面的研究，E-mail: zjfang@sues.edu.cn
更新日期/Last Update: 2021-10-29