S. Warfield, J. Dengler, J. Zaers, C. Guttmann, W. Wells, G. Ettinger, J. Hiller, R. Kikinis
{"title":"实验室研究:从MRI中自动识别灰质结构以改善白质病变的分割","authors":"S. Warfield, J. Dengler, J. Zaers, C. Guttmann, W. Wells, G. Ettinger, J. Hiller, R. Kikinis","doi":"10.3109/10929089509106339","DOIUrl":null,"url":null,"abstract":"The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML.We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier.Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the c...","PeriodicalId":79505,"journal":{"name":"Journal of image guided surgery","volume":"1 1","pages":"326-338"},"PeriodicalIF":0.0000,"publicationDate":"1995-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3109/10929089509106339","citationCount":"14","resultStr":"{\"title\":\"Laboratory Investigation:Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions\",\"authors\":\"S. Warfield, J. Dengler, J. Zaers, C. Guttmann, W. Wells, G. Ettinger, J. Hiller, R. Kikinis\",\"doi\":\"10.3109/10929089509106339\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML.We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier.Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the c...\",\"PeriodicalId\":79505,\"journal\":{\"name\":\"Journal of image guided surgery\",\"volume\":\"1 1\",\"pages\":\"326-338\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3109/10929089509106339\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of image guided surgery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3109/10929089509106339\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of image guided surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3109/10929089509106339","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Laboratory Investigation:Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions
The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML.We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier.Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the c...