[1]浦秀丽,刘翔,汤显,等.基于边缘监督的肝部超声图像包膜分割网络[J].中国医学物理学杂志,2022,39(10):1255-1262.[doi:DOI:10.3969/j.issn.1005-202X.2022.10.013]
 PU Xiuli,LIU Xiang,TANG Xian,et al.Liver capsule segmentation in ultrasound image using edge supervision based network[J].Chinese Journal of Medical Physics,2022,39(10):1255-1262.[doi:DOI:10.3969/j.issn.1005-202X.2022.10.013]
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基于边缘监督的肝部超声图像包膜分割网络()
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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10期
页码:
1255-1262
栏目:
医学影像物理
出版日期:
2022-11-02

文章信息/Info

Title:
Liver capsule segmentation in ultrasound image using edge supervision based network
文章编号:
1005-202X(2022)10-1255-08
作者:
浦秀丽1刘翔1汤显1宋家琳2
1.上海工程技术大学电子电气工程学院, 上海 201620; 2.中国人民解放军第二军医大学长征医院超声诊疗科, 上海 200003
Author(s):
PU Xiuli1 LIU Xiang1 TANG Xian1 SONG Jialin2
1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Ultrasound Diagnosis and Treatment, Shanghai Changzheng Hospital, the Second Military Medical University, Shanghai 200003, China
关键词:
UNet肝包膜边缘监督空洞卷积图像分割
Keywords:
Keywords: UNet liver capsule edge supervision atrous convolution image segmentation
分类号:
R318;TP301.6
DOI:
DOI:10.3969/j.issn.1005-202X.2022.10.013
文献标志码:
A
摘要:
肝纤维化、肝硬化的早期发现对临床治疗和预后评估具有重要意义。而肝包膜的形态和纹理特征是计算机辅助肝硬化诊断的重要依据。本文提出一种基于边缘监督的肝部超声图像包膜分割网络。该网络以常用的分割模型UNet为基础,引入空洞卷积,扩大感受野;同时,添加了边缘监督模块,从而将特征学习主要聚焦在图像梯度较大的部分;此外,还设计了混合加权损失函数,来缓解肝包膜部分与其他区域之间的极度不平衡情况。实验结果表明,本文提出的ES-UNet网络结构平均Dice系数相比原始UNet提高了0.171 5,平均交并比(MIoU)提高了0.021 5,其他指标也有较明显的提高,可见,本文算法的各个组件对模型分割性能的优化都有一定的贡献,改进后的模型可以实现肝包膜的精确分割。
Abstract:
Abstract: The early detection of liver fibrosis and liver cirrhosis is of great significance for clinical treatment and prognosis evaluation, and the morphological and texture characteristics of liver capsule are important for the computer-assisted diagnosis of liver cirrhosis. An edge supervision based network (ES-UNet) is proposed for liver capsule segmentation in ultrasound images. Based on the commonly used segmentation model (UNet), ES-UNet uses atrous convolution to expand the receptive field, and edge supervision module to focus the feature learning on the region with large image gradient. In addition, a mixed weighted loss function is used to reduce the extreme imbalance between the liver capsule and other regions. The experimental results show that compared with those of original UNet model, the average Dice coefficient of ES-UNet is increased by 0.171 5, and the mean intersection over union is higher by 0.021 5, and the other indicators are also elevated significantly, indicating that each component of the proposed algorithm has a certain contribution to the optimization of model segmentation performance. The improved model can achieve accurate liver capsule segmentation.

相似文献/References:

[1]陈洪涛,郑芳,高艳,等.基于边缘强化的Unet-TIC模型对前列腺自动勾画研究[J].中国医学物理学杂志,2022,39(6):719.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.011]
 CHEN Hongtao,ZHENG Fang,GAO Yan,et al.Automatic prostate segmentation with boundary-enhanced Unet-TIC model[J].Chinese Journal of Medical Physics,2022,39(10):719.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.011]

备注/Memo

备注/Memo:
【收稿日期】2022-05-10 【基金项目】上海市自然科学基金(19ZR1421500) 【作者简介】浦秀丽,在读硕士,研究方向:医学图像处理,E-mail: 18351987963@163.com 【通信作者】刘翔,博士,副教授,研究方向:计算机视觉,机器智能,E-mail: xliu@sues.edu.cn
更新日期/Last Update: 2022-10-27