Detection of Lymph Nodes using CNN from Contrast-Enhanced CT Images

T. Ono, Y. Iwahori, Hiroyasu Usami, B. Kijsirikul, M. Bhuyan, Taihei Oshiro, Y. Shimizu
{"title":"Detection of Lymph Nodes using CNN from Contrast-Enhanced CT Images","authors":"T. Ono, Y. Iwahori, Hiroyasu Usami, B. Kijsirikul, M. Bhuyan, Taihei Oshiro, Y. Shimizu","doi":"10.1109/IIAI-AAI50415.2020.00100","DOIUrl":null,"url":null,"abstract":"Detection of lymph nodes in medical practice is important to determine the presence or absence of cancer metastasis or to select the medical operation based on the degree of cancer progression. However, there are problems such that the number of medical doctors is limited or it is difficult to perform the medical diagnosis with high accuracy. This paper tries to solve these problems and proposes a new approach to detect lymph nodes by learning the status of lymph nodes with R2U-Net using divided patches of contrast-enhanced CT images. It is shown that the detection of lymph node becomes better by introducing the integration processing.","PeriodicalId":188870,"journal":{"name":"2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI50415.2020.00100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Detection of lymph nodes in medical practice is important to determine the presence or absence of cancer metastasis or to select the medical operation based on the degree of cancer progression. However, there are problems such that the number of medical doctors is limited or it is difficult to perform the medical diagnosis with high accuracy. This paper tries to solve these problems and proposes a new approach to detect lymph nodes by learning the status of lymph nodes with R2U-Net using divided patches of contrast-enhanced CT images. It is shown that the detection of lymph node becomes better by introducing the integration processing.
利用CNN从增强CT图像中检测淋巴结
在医疗实践中,淋巴结的检测对于确定是否存在肿瘤转移或根据肿瘤进展程度选择医疗手术具有重要意义。然而,存在着医生数量有限或难以进行高精度医疗诊断等问题。本文试图解决这些问题,提出了一种利用对比增强CT图像的分割斑块,利用R2U-Net学习淋巴结状态来检测淋巴结的新方法。结果表明,引入积分处理后,淋巴结的检测效果更好。
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:604180095
Book学术官方微信