Marco T Y Schneider, Ju Zhang, Joseph J Crisco, Arnold-Peter C Weiss, Amy L Ladd, Poul M F Nielsen, Thor Besier
{"title":"基于参数统计形状建模和随机森林回归投票的拇指梯形-掌骨关节自动分割。","authors":"Marco T Y Schneider, Ju Zhang, Joseph J Crisco, Arnold-Peter C Weiss, Amy L Ladd, Poul M F Nielsen, Thor Besier","doi":"10.1080/21681163.2018.1501765","DOIUrl":null,"url":null,"abstract":"<p><p>We propose an automatic pipeline for creating shape modelling suitable parametric meshes of the trapeziometacarpal (TMC) joint from clinical CT images for the purpose of batch processing and analysis. The method uses 3D random forest regression voting (RFRV) with statistical shape model (SSM) segmentation. The method was demonstrated in a validation experiment involving 65 CT images, 15 of which were randomly selected to be excluded from the training set for testing. With mean root mean squared (RMS) errors of 1.066 mm and 0.632 mm for the first metacarpal and trapezial bones respectively, and a segmentation time of ~2 minutes per CT image, the preliminary results showed promise for providing accurate 3D meshes of TMC joint bones for batch processing.</p>","PeriodicalId":72671,"journal":{"name":"","volume":"7 3","pages":"297-301"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/21681163.2018.1501765","citationCount":"3","resultStr":"{\"title\":\"Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting.\",\"authors\":\"Marco T Y Schneider, Ju Zhang, Joseph J Crisco, Arnold-Peter C Weiss, Amy L Ladd, Poul M F Nielsen, Thor Besier\",\"doi\":\"10.1080/21681163.2018.1501765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We propose an automatic pipeline for creating shape modelling suitable parametric meshes of the trapeziometacarpal (TMC) joint from clinical CT images for the purpose of batch processing and analysis. The method uses 3D random forest regression voting (RFRV) with statistical shape model (SSM) segmentation. The method was demonstrated in a validation experiment involving 65 CT images, 15 of which were randomly selected to be excluded from the training set for testing. With mean root mean squared (RMS) errors of 1.066 mm and 0.632 mm for the first metacarpal and trapezial bones respectively, and a segmentation time of ~2 minutes per CT image, the preliminary results showed promise for providing accurate 3D meshes of TMC joint bones for batch processing.</p>\",\"PeriodicalId\":72671,\"journal\":{\"name\":\"\",\"volume\":\"7 3\",\"pages\":\"297-301\"},\"PeriodicalIF\":0.0,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/21681163.2018.1501765\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21681163.2018.1501765\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/7/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21681163.2018.1501765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/7/26 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting.
We propose an automatic pipeline for creating shape modelling suitable parametric meshes of the trapeziometacarpal (TMC) joint from clinical CT images for the purpose of batch processing and analysis. The method uses 3D random forest regression voting (RFRV) with statistical shape model (SSM) segmentation. The method was demonstrated in a validation experiment involving 65 CT images, 15 of which were randomly selected to be excluded from the training set for testing. With mean root mean squared (RMS) errors of 1.066 mm and 0.632 mm for the first metacarpal and trapezial bones respectively, and a segmentation time of ~2 minutes per CT image, the preliminary results showed promise for providing accurate 3D meshes of TMC joint bones for batch processing.