{"id":12076,"date":"2020-06-29T09:36:52","date_gmt":"2020-06-29T09:36:52","guid":{"rendered":"https:\/\/bgrssb.icgbio.ru\/2020\/2020\/06\/29\/the-siberian-multimodal-brain-tumor-image-segmentation-dataset-sbt\/"},"modified":"2020-06-29T09:36:52","modified_gmt":"2020-06-29T09:36:52","slug":"the-siberian-multimodal-brain-tumor-image-segmentation-dataset-sbt","status":"publish","type":"post","link":"https:\/\/bgrssb.icgbio.ru\/2020\/2020\/06\/29\/the-siberian-multimodal-brain-tumor-image-segmentation-dataset-sbt\/","title":{"rendered":"The Siberian multimodal brain tumor image segmentation dataset (SBT)"},"content":{"rendered":"<p>Poster (<a href=\"https:\/\/bgrssb.icgbio.ru\/wp-content\/uploads\/2020\/07\/514.pdf\">download<\/a>)<br \/>\n    <br \/><a href=\"https:\/\/bgrssb.icgbio.ru\/wp-content\/uploads\/2020\/07\/514.pdf\" class=\"pdfemb-viewer\" style=\"\" data-width=\"max\" data-height=\"max\"  data-toolbar=\"bottom\" data-toolbar-fixed=\"off\">514<br\/><\/a><br \/>Sergey Golushko<sup>1<\/sup>, Mikhail Amelin<sup>2<\/sup>, Bair Tuchinov<sup>3<\/sup>, Evgeniya Amelina<sup>4<\/sup>, Nikolay Tolstokulakov<sup>5<\/sup>, Evgeniy Pavlovskiy<sup>6<\/sup>, Vladimir Groza<sup>7<\/sup><br \/><sup>1<\/sup>Novosibirsk State University, s.k.golushko@gmail.com<br \/><sup>2<\/sup>FSBI \\&#8221;Federal Neurosurgical Center\\&#8221;, amelin81@gmail.com<br \/><sup>3<\/sup>Novosibirsk State University, bairts@gmail.com<br \/><sup>4<\/sup>Novosibirsk State University, amelina.evgenia@gmail.com<br \/><sup>5<\/sup>Novosibirsk State University, n.tolstokulakov@g.nsu.ru<br \/><sup>6<\/sup>Novosibirsk State University, pavlovskiy@post.nsu.ru<br \/><sup>7<\/sup>Median Technologies, vladimir.groza@gmail.com<\/p>\n<p>Automatic brain tumor segmentation from CT or MRI scans is one of the crucial problems among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying patients with various pathologies.In this paper, we report the results of the research project \\&#8221;Brain Tumor Segmentation\\&#8221; organized in conjunction with the Federal Neurosurgical Center. Several state-of-the-art tumor segmentation algorithms were applied to a set of 100 MRI scans of meningioma, neurinoma and glioma patients &#8211; manually annotated by up to three raters &#8211; and to 100 comparable scans obtained using the automated tumor multi-region segmentation. Quantitative evaluations revealed a considerable agreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 85-90\\\\%). We found that different algorithms worked best for different sub-regions, but no single algorithm ranked in the top for all subregions simultaneously.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Poster (download) Sergey Golushko1, Mikhail Amelin2, Bair Tuchinov3, Evgeniya Amelina4, Nikolay Tolstokulakov5, Evgeniy Pavlovskiy6, Vladimir Groza71Novosibirsk State University, s.k.golushko@gmail.com2FSBI \\&#8221;Federal Neurosurgical Center\\&#8221;, amelin81@gmail.com3Novosibirsk State University, bairts@gmail.com4Novosibirsk State University, amelina.evgenia@gmail.com5Novosibirsk State University, n.tolstokulakov@g.nsu.ru6Novosibirsk State University, pavlovskiy@post.nsu.ru7Median Technologies, vladimir.groza@gmail.com Automatic brain tumor segmentation from CT or MRI scans is one of the crucial problems among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying patients with various pathologies.In this paper, we report the results of the research project \\&#8221;Brain Tumor Segmentation\\&#8221; organized in conjunction with the Federal Neurosurgical Center. Several state-of-the-art tumor segmentation algorithms were applied to a set of 100 MRI scans of meningioma, neurinoma and glioma patients &#8211; manually annotated by up to three raters &#8211; and to 100 comparable scans obtained using the automated tumor multi-region segmentation. Quantitative evaluations revealed a considerable agreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 85-90\\\\%). We found that different algorithms worked best for different sub-regions, but no single algorithm ranked in the top for all subregions simultaneously.<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2165],"tags":[1986,2215,2188,2214],"_links":{"self":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/posts\/12076"}],"collection":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/comments?post=12076"}],"version-history":[{"count":0,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/posts\/12076\/revisions"}],"wp:attachment":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/media?parent=12076"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/categories?post=12076"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/tags?post=12076"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}