[1]巩稼民,杨红蕊,郭庆庆,等.基于分步目标定位的腰椎间盘自动诊断方法[J].中国医学物理学杂志,2021,38(3):317-322.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.009]
 GONG Jiamin,YANG Hongrui,et al.Automatic diagnosis of lumbar intervertebral disc herniation based on step-by-step target positioning[J].Chinese Journal of Medical Physics,2021,38(3):317-322.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.009]
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基于分步目标定位的腰椎间盘自动诊断方法()
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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3期
页码:
317-322
栏目:
医学影像物理
出版日期:
2021-03-30

文章信息/Info

Title:
Automatic diagnosis of lumbar intervertebral disc herniation based on step-by-step target positioning
文章编号:
1005-202X(2021)03-0317-06
作者:
巩稼民12杨红蕊1郭庆庆2蒋杰伟2潘琼3马豆豆2高燕军4
1.西安邮电大学通信与信息工程学院, 陕西 西安 710121; 2.西安邮电大学电子工程学院, 陕西 西安 710121; 3.西北农林科技大学理学院, 陕西 西安 712100; 4.西安市第三医院医学影像科, 陕西 西安 710071
Author(s):
GONG Jiamin1 2 YANG Hongrui1 GUO Qingqing2 JIANG Jiewei2 PAN Qiong3 MA Doudou2 GAO Yanjun4
1. School of Communication and Information Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 2. School of Electronic Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 3. School of Science, Northwest Agriculture and Forestry University, Xi an 712100, China 4. Department of Medical Imaging, Xian No.3 Hospital, Xian 710071, China
关键词:
腰椎间盘突出分步目标定位Faster R-CNN网络改进的残差卷积神经网络计算机辅助诊断系统
Keywords:
Keywords: lumbar intervertebral disc herniation step-by-step target positioning Faster R-CNN network improved residual convolutional neural network computer-aided diagnosis system
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2021.03.009
文献标志码:
A
摘要:
针对当前腰椎间盘自动诊断方法存在的准确率偏低的问题,提出一种基于分步目标定位的计算机辅助诊断方法。该方法首先使用Faster R-CNN目标定位网络预处理腰椎间盘影像,去除韧带以及周围噪声区域,获得腰椎间盘的轮廓区域;然后放大定位的间盘轮廓3倍,再次利用Faster R-CNN网络精细化定位病灶区域,从而解决因病灶目标太小而无法准确定位的问题;最后,将病灶区域输入到改进的残差卷积神经网络中以提取高层特征和严重性分级,改进的残差卷积神经网络(ResNet-20)通过建立短路机制以提高分类器的准确率。实验结果表明,相较于传统的诊断方法,该方法将腰椎间盘突出的诊断准确率提升5.1%。
Abstract:
Abstract: A computer-aided diagnosis method based on step-by-step target positioning (SSTP) is proposed for solving the problem of low accuracy in current methods for the automatic diagnosis of lumbar intervertebral disc herniation. Firstly, Faster R-CNN target positioning network is used to preprocess lumbar intervertebral disc images, remove ligaments and surrounding noise areas, and obtain the contour of lumbar intervertebral disc. Then, the contour of the located disc is enlarged by 3 times, and Faster R-CNN network is further applied to finely locate the focus area, thus solving the problem of inaccurate positioning due to the small focus. Finally, the focus area is input into the improved residual convolution neural network to extract high-level features and to grade the severity. The improved residual convolutional neural network (ResNet-20) improves the classifier accuracy by establishing a short-circuit mechanism. Experimental results show that the proposed method improves diagnostic accuracy of lumbar intervertebral disc herniation by 5.1% in comparison with traditional diagnostic methods.

备注/Memo

备注/Memo:
【收稿日期】2020-10-19 【基金项目】国家重点研发计划(2018YFC0116500);中央高校基本科研业务费专项资金资助项目(JB181002) 【作者简介】巩稼民,博士,教授,研究生导师,研究方向:光通信和图像处理、医疗影像处理,E-mail: gjm@xupt.edu.cn
更新日期/Last Update: 2021-03-30