Xiaoguang Lu, B. Georgescu, Yefeng Zheng, Joanne Otsuki, D. Comaniciu
{"title":"AutoMPR: Automatic detection of standard planes in 3D echocardiography","authors":"Xiaoguang Lu, B. Georgescu, Yefeng Zheng, Joanne Otsuki, D. Comaniciu","doi":"10.1109/ISBI.2008.4541237","DOIUrl":null,"url":null,"abstract":"3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2008.4541237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33
Abstract
3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.