{"title":"Computer-based segmentation, change detection and quantification for lesions in multiple sclerosis","authors":"E. Goceri, Caner Songül","doi":"10.1109/UBMK.2017.8093371","DOIUrl":null,"url":null,"abstract":"Multiple Sclerosis (MS) is a neurological, progressive widespread disease whose diagnosis, treatment and monitoring have vital importance. However, manual method based on visual inspection for diagnosis and time-series assessments of changes in MS lesions is not re-producible and quantitative. Also, it is subjective and yields in inter-/intra-observer variabilities. Furthermore, the conventional method is time-consuming and mostly urgent results are required in practice. Therefore, in the literature, automated techniques have been proposed to detect and segment MS lesions and also to evaluate changes in these lesions quantitatively. In this paper, these automated approaches are presented for the radiologists, neurologists and researchers who are interested in this subject or want to improve former works or want to develop novel automated methods to overcome the problems in this active research area.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computer Science and Engineering (UBMK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UBMK.2017.8093371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Multiple Sclerosis (MS) is a neurological, progressive widespread disease whose diagnosis, treatment and monitoring have vital importance. However, manual method based on visual inspection for diagnosis and time-series assessments of changes in MS lesions is not re-producible and quantitative. Also, it is subjective and yields in inter-/intra-observer variabilities. Furthermore, the conventional method is time-consuming and mostly urgent results are required in practice. Therefore, in the literature, automated techniques have been proposed to detect and segment MS lesions and also to evaluate changes in these lesions quantitatively. In this paper, these automated approaches are presented for the radiologists, neurologists and researchers who are interested in this subject or want to improve former works or want to develop novel automated methods to overcome the problems in this active research area.