Self-organizing maps as a tool for segmentation of Magnetic Resonance Imaging (MRI) of relapsing-remitting multiple sclerosis

P. Mei, C. C. Carneiro, M. Kuroda, S. Fraser, L. Min, F. Reis
{"title":"Self-organizing maps as a tool for segmentation of Magnetic Resonance Imaging (MRI) of relapsing-remitting multiple sclerosis","authors":"P. Mei, C. C. Carneiro, M. Kuroda, S. Fraser, L. Min, F. Reis","doi":"10.1109/WSOM.2017.8020005","DOIUrl":null,"url":null,"abstract":"Multiple Sclerosis (MS) is the most prevalent demyelinating disease of the Central Nervous System, being the Relapsing-Remitting (RRMS) its most common subtype. We explored here the viability of use of Self Organizing Maps (SOM) to perform automatic segmentation of MS lesions apart from CNS normal tissue. SOM were able, in most cases, to successfully segment MRIs of patients with RRMS, with the correct separation of normal versus pathological tissue especially in supratentorial acquisitions, although it could not differentiate older from newer lesions.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WSOM.2017.8020005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Multiple Sclerosis (MS) is the most prevalent demyelinating disease of the Central Nervous System, being the Relapsing-Remitting (RRMS) its most common subtype. We explored here the viability of use of Self Organizing Maps (SOM) to perform automatic segmentation of MS lesions apart from CNS normal tissue. SOM were able, in most cases, to successfully segment MRIs of patients with RRMS, with the correct separation of normal versus pathological tissue especially in supratentorial acquisitions, although it could not differentiate older from newer lesions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
自组织图作为复发缓解型多发性硬化症磁共振成像(MRI)分割的工具
多发性硬化症(MS)是最常见的中枢神经系统脱髓鞘疾病,复发缓解(RRMS)是其最常见的亚型。我们在此探讨了使用自组织图(SOM)对MS病变与CNS正常组织进行自动分割的可行性。在大多数情况下,SOM能够成功地分割RRMS患者的mri,正确分离正常组织和病理组织,特别是在幕上病变中,尽管它不能区分新旧病变。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Empirical evaluation of gradient methods for matrix learning vector quantization Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning Prototypes and matrix relevance learning in complex fourier space Imputation of reactive silica and available alumina in bauxites by self-organizing maps An evolutionary building algorithm for Deep Neural Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1