Abstract<\/h2>\n
Labeling or segmentation<\/span> of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common techniques to obtain these labels are through either fully automated segmentation<\/span> or through multi-atlas based segmentation<\/span> and label<\/span> fusion<\/span>. Fully automated techniques often result in highly accurate segmentations but lack the robustness to be viable in many cases. On the other hand, label<\/span> fusion<\/span> techniques are often extremely robust, but lack the accuracy of automated algorithms for specific classes of problems. Herein, we propose to perform simultaneous<\/span> automated segmentation<\/span> and statistical<\/span> label<\/span> fusion<\/span> through the reformulation of a generative model to include a linkage structure that explicitly estimates the complex global relationships between labels and intensities. These relationships are inferred from the atlas labels and intensities and applied to the target using a non-parametric approach. The novelty of this approach lies in the combination of previously exclusive techniques and attempts to combine the accuracy benefits of automated segmentation<\/span> with the robustness of a multi-atlas based approach. The accuracy benefits of this simultaneous<\/span> approach are assessed using a multi-label<\/span> multi- atlas whole-brain segmentation<\/span> experiment and the segmentation<\/span> of the highly variable thyroid on computed tomography images. The results demonstrate that this technique has major benefits for certain types of problems and has the potential to provide a paradigm shift in which the lines between statistical<\/span> label<\/span> fusion<\/span> and automated segmentation<\/span> are dramatically blurred.<\/p>\n A. Asman and B. Landman. \u201cSimultaneous Segmentation and Statistical Label Fusion.\u201d In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) \u2020 Full Text: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24357909 Abstract Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common…<\/p>\n","protected":false},"author":6300,"featured_media":657,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,46,4],"tags":[75,98],"class_list":["post-656","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-image-segmentation","category-multi-atlas-segmentation","category-neuroimaging","tag-brain","tag-thyroid"],"_links":{"self":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/656","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/6300"}],"replies":[{"embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=656"}],"version-history":[{"count":2,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/656\/revisions"}],"predecessor-version":[{"id":760,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/656\/revisions\/760"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/657"}],"wp:attachment":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=656"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=656"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=656"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"nihms-342590-f0001\"](\"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-content\/uploads\/sites\/2304\n2661\/2016\/11\/nihms-342590-f0001.jpg\")