{"id":163441,"date":"2012-10-01T00:00:00","date_gmt":"2012-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/context-sensitive-classification-forests-for-segmentation-of-brain-tumor-tissues\/"},"modified":"2022-12-09T09:34:30","modified_gmt":"2022-12-09T17:34:30","slug":"context-sensitive-classification-forests-for-segmentation-of-brain-tumor-tissues","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/context-sensitive-classification-forests-for-segmentation-of-brain-tumor-tissues\/","title":{"rendered":"Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We describe our submission to the Brain Tumor Segmentation Challenge (BraTS) at MICCAI 2012, which is based on our method for tissue-specific segmentation of high-grade brain tumors [3]. The main idea is to cast the segmentation as a classification task, and use the discriminative power of context information. We realize this idea by equipping a classification forest (CF) with spatially non-local features to represent the data, and by providing the CF with initial probability estimates for the single tissue classes as additional input (along-side the MRI channels). The initial probabilities are patient-specific, and computed at test time based on a learned model of intensity. Through the combination of the initial probabilities and the non-local features, our approach is able to capture the context information for each data point. Our method is fully automatic, with segmentation run times in the range of 1-2 minutes per patient. We evaluate the submission by cross-validation on the real and synthetic, high- and low-grade tumor BraTS data sets.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe our submission to the Brain Tumor Segmentation Challenge (BraTS) at MICCAI 2012, which is based on our method for tissue-specific segmentation of high-grade brain tumors [3]. The main idea is to cast the segmentation as a classification task, and use the discriminative power of context information. We realize this idea by equipping a 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Criminisi","user_id":41790,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Antonio Criminisi"},{"type":"user_nicename","value":"Darko Zikic","user_id":31543,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Darko Zikic"},{"type":"user_nicename","value":"Ben Glocker","user_id":31887,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ben Glocker"},{"type":"user_nicename","value":"Jamie Shotton","user_id":32162,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jamie 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