Hana Bouchouicha, Olfa Ghribi, A. Hamida, C. Mhiri, M. Dammak, K. B. Mahfoudh, O. Kammoun
{"title":"基于分割方法比较的胶质母细胞瘤MRT探查:迈向一种先进的临床辅助工具","authors":"Hana Bouchouicha, Olfa Ghribi, A. Hamida, C. Mhiri, M. Dammak, K. B. Mahfoudh, O. Kammoun","doi":"10.1109/ATSIP.2018.8364472","DOIUrl":null,"url":null,"abstract":"Glioblastoma delineation and its related active region specification are still a real challenge and so difficult essentially due to their multiform aspect. in fact, this type of tumors is very invasive and appears as non-enhancing region and with various forms on magnetic resonance imaging modalities. Thus, Glioblastoma segmentation is challenging especially in differentiating between white matter and edema, necrosis and gray matter due to their homogeneity in intensity and texture. An accurate delineation of the tumor is necessary for the tumor progress evaluation and medical treatment efficacy assessment. in addition, a precise limitation of the tumor is mandatory in surgical and Radio Therapies. Manual segmentation methods have been always used and require radiologist intervention and could be also used as reference. our attention was for the benefits to extract from the semi-Automatic segmentation Methods and the Fully Automatic segmentation Methods, and this would yield a real complementarity giving hence one complete and rich convivial clinical aided tool. This paper presents therefore a useful review of these methods proposed for the Glioblastoma MRI segmentation.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Glioblastoma MRT exploration based on segmentation methods' comparison: Towards an advanced clinical aided tool\",\"authors\":\"Hana Bouchouicha, Olfa Ghribi, A. Hamida, C. Mhiri, M. Dammak, K. B. Mahfoudh, O. Kammoun\",\"doi\":\"10.1109/ATSIP.2018.8364472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Glioblastoma delineation and its related active region specification are still a real challenge and so difficult essentially due to their multiform aspect. in fact, this type of tumors is very invasive and appears as non-enhancing region and with various forms on magnetic resonance imaging modalities. Thus, Glioblastoma segmentation is challenging especially in differentiating between white matter and edema, necrosis and gray matter due to their homogeneity in intensity and texture. An accurate delineation of the tumor is necessary for the tumor progress evaluation and medical treatment efficacy assessment. in addition, a precise limitation of the tumor is mandatory in surgical and Radio Therapies. Manual segmentation methods have been always used and require radiologist intervention and could be also used as reference. our attention was for the benefits to extract from the semi-Automatic segmentation Methods and the Fully Automatic segmentation Methods, and this would yield a real complementarity giving hence one complete and rich convivial clinical aided tool. This paper presents therefore a useful review of these methods proposed for the Glioblastoma MRI segmentation.\",\"PeriodicalId\":332253,\"journal\":{\"name\":\"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)\",\"volume\":\"76 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATSIP.2018.8364472\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATSIP.2018.8364472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Glioblastoma MRT exploration based on segmentation methods' comparison: Towards an advanced clinical aided tool
Glioblastoma delineation and its related active region specification are still a real challenge and so difficult essentially due to their multiform aspect. in fact, this type of tumors is very invasive and appears as non-enhancing region and with various forms on magnetic resonance imaging modalities. Thus, Glioblastoma segmentation is challenging especially in differentiating between white matter and edema, necrosis and gray matter due to their homogeneity in intensity and texture. An accurate delineation of the tumor is necessary for the tumor progress evaluation and medical treatment efficacy assessment. in addition, a precise limitation of the tumor is mandatory in surgical and Radio Therapies. Manual segmentation methods have been always used and require radiologist intervention and could be also used as reference. our attention was for the benefits to extract from the semi-Automatic segmentation Methods and the Fully Automatic segmentation Methods, and this would yield a real complementarity giving hence one complete and rich convivial clinical aided tool. This paper presents therefore a useful review of these methods proposed for the Glioblastoma MRI segmentation.