Predicting tumor location from prone to supine breast MRI using a simulation of breast deformation

Hong Song, Xiangbin Zhu, Xiangfei Cui
{"title":"Predicting tumor location from prone to supine breast MRI using a simulation of breast deformation","authors":"Hong Song, Xiangbin Zhu, Xiangfei Cui","doi":"10.1109/GrC.2013.6740419","DOIUrl":null,"url":null,"abstract":"Breast cancer is one of the biggest killers to women, and early diagnosis is essential for improved prognosis. The shape of the breast varies hugely between the scenarios of magnetic resonance (MR) imaging (patient lies prone, breast hanging down under gravity) and ultrasound (patient lies supine). Matching between such pairs of images is considered essential by radiologists for more reliable diagnosis of early breast cancer. In this paper, a method to predict tumor location by simulating the breast deformation from breast in the prone position to the compressed breast in the supine position was developed, which is based on a 3-D patient-specific breast model constructed from MR images with the use of the finite-element method and nonlinear elasticity. The performance was assessed by the mean distance between corresponding lesion locations for three cases. A mean accuracy of 4.94mm in Euclidean distance was achieved by using the proposed method. Experiments using actual images show that the method gives good predictions which can be used to find exact correspondences between tumors location in prone and supine breast images.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Granular Computing (GrC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GrC.2013.6740419","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Breast cancer is one of the biggest killers to women, and early diagnosis is essential for improved prognosis. The shape of the breast varies hugely between the scenarios of magnetic resonance (MR) imaging (patient lies prone, breast hanging down under gravity) and ultrasound (patient lies supine). Matching between such pairs of images is considered essential by radiologists for more reliable diagnosis of early breast cancer. In this paper, a method to predict tumor location by simulating the breast deformation from breast in the prone position to the compressed breast in the supine position was developed, which is based on a 3-D patient-specific breast model constructed from MR images with the use of the finite-element method and nonlinear elasticity. The performance was assessed by the mean distance between corresponding lesion locations for three cases. A mean accuracy of 4.94mm in Euclidean distance was achieved by using the proposed method. Experiments using actual images show that the method gives good predictions which can be used to find exact correspondences between tumors location in prone and supine breast images.
利用模拟乳房变形的乳房MRI预测从俯卧到仰卧的肿瘤位置
乳腺癌是女性的最大杀手之一,早期诊断对于改善预后至关重要。乳房的形状在磁共振成像(病人俯卧,乳房在重力作用下下垂)和超声(病人仰卧)的情况下差别很大。放射科医生认为,为了更可靠地诊断早期乳腺癌,这些图像对之间的匹配是必不可少的。本文采用有限元法和非线性弹性理论,基于磁共振图像构建的患者特异性乳房三维模型,提出了一种通过模拟乳房从俯卧位到仰卧位压缩的乳房变形来预测肿瘤位置的方法。通过三个病例对应病灶位置之间的平均距离来评估其性能。该方法在欧氏距离上的平均精度为4.94mm。实际图像的实验表明,该方法可以很好地预测俯卧和仰卧乳房图像中肿瘤位置的精确对应关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信