{"id":366,"date":"2012-12-31T12:18:25","date_gmt":"2012-12-31T17:18:25","guid":{"rendered":"https:\/\/my.vanderbilt.edu\/masi\/?p=366"},"modified":"2016-10-31T12:28:16","modified_gmt":"2016-10-31T17:28:16","slug":"out-of-atlas-labeling-a-multi-atlas-approach-to-cancer-segmentation","status":"publish","type":"post","link":"https:\/\/my.vanderbilt.edu\/masi\/2012\/12\/out-of-atlas-labeling-a-multi-atlas-approach-to-cancer-segmentation\/","title":{"rendered":"Out-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation"},"content":{"rendered":"<p>Andrew J. Asman and Bennett A. Landman, \u201cOut-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation\u201d, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain\u2020<\/p>\n<p><strong>Full Text: <\/strong>https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3892947\/<\/p>\n<p>&nbsp;<\/p>\n<h2>Abstract<\/h2>\n<div>\n<p id=\"P1\" class=\"p p-first-last\">Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs wildly from expectations \u2013 as is often the case in brain cancers. We propose a novel out-of-atlas technique to estimate the spatial extent of abnormal brain regions by combining multi-atlas based segmentation with semi-local non-parametric intensity analysis. In a study with 30 clinically-acquired MRI scans of patients with malignant gliomas and 29 atlases of normal anatomy from research acquisitions, we demonstrate that this technique robustly identifies cancerous regions. The resulting segmentations could be used to study cancer morphometrics or guide selection\/application\/refinement of tumor analysis models or regional image quantification approaches.<\/p>\n<\/div>\n<div class=\"sec\"><strong class=\"kwd-title\">Keywords: <\/strong><span class=\"kwd-text\">Cancer Segmentation, Tumors, Multi-Atlas Segmentation, Out-of-Atlas Labeling<\/span><\/div>\n<div class=\"sec\"><\/div>\n<div class=\"sec\">\n<figure id=\"attachment_388\" aria-describedby=\"caption-attachment-388\" style=\"width: 500px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-388\" src=\"https:\/\/my.vanderbilt.edu\/masi\/wp-content\/uploads\/sites\/2304\n2661\/2016\/10\/asman5.png\" alt=\"Example qualitative results. Three representative slices from three different brains are presented for comparison. For each example, the target, truth labels, an example atlas, the label fusion estimate and the cancer likelihood estimate are presented. Note the high quality likelihood estimate in the first two examples. On the third example, the cancer is detected, but it is not as well localized as in the first two examples. \" width=\"500\" height=\"309\" \/><figcaption id=\"caption-attachment-388\" class=\"wp-caption-text\">Example qualitative results. Three representative slices from three different brains are presented for comparison. For each example, the target, truth labels, an example atlas, the label fusion estimate and the cancer likelihood estimate are presented. Note the high quality likelihood estimate in the first two examples. On the third example, the cancer is detected, but it is not as well localized as in the first two examples.<\/figcaption><\/figure>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Andrew J. Asman and Bennett A. Landman, \u201cOut-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation\u201d, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain\u2020 Full Text: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3892947\/ &nbsp; Abstract Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs&#8230;<\/p>\n","protected":false},"author":6319,"featured_media":388,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9,46,4],"tags":[25,59,22,12],"class_list":["post-366","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diffusion-weighted-mri","category-multi-atlas-segmentation","category-neuroimaging","tag-abdomen-segmentation","tag-cancer","tag-label","tag-multi-atlas"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/366","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/6319"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=366"}],"version-history":[{"count":1,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/366\/revisions"}],"predecessor-version":[{"id":392,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/366\/revisions\/392"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/388"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=366"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}