{"id":906051,"date":"2022-12-09T06:42:13","date_gmt":"2022-12-09T14:42:13","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-12-21T02:27:30","modified_gmt":"2022-12-21T10:27:30","slug":"multi-atlas-label-propagation-with-atlas-encoding-by-randomized-forests-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/multi-atlas-label-propagation-with-atlas-encoding-by-randomized-forests-2\/","title":{"rendered":"Multi-Atlas Label Propagation with Atlas Encoding by Randomized Forests [Special Left Field Award]"},"content":{"rendered":"<p>We describe our submission to the MICCAI 2013 SATA Challenge. The method is based on multi-atlas based label propagation, its major characteristic being that is uses the concept of an atlas forest to represent an atlas. This results in an efficient scheme, which requires only a single registration to label a target. Fusion of the probabilistic label proposals from each atlas is done by averaging across atlases. Results are submitted for the unregistered Diencephalon data set.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe our submission to the MICCAI 2013 SATA Challenge. The method is based on multi-atlas based label propagation, its major characteristic being that is uses the concept of an atlas forest to represent an atlas. This results in an efficient scheme, which requires only a single registration to label a target. Fusion of the 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