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Patch-based Segmentation of Brain Tissues

Abstract : We describe our submission to the Brain Tumor Segmentation Challenge (BraTS) at MICCAI 2013. This segmentation approach is based on similarities between multi-channel patches. After patches are extracted from several MR channels for a test case, similar patches are found in training images for which label maps are known. These labels maps are then combined to result in a segmentation map for the test case. The labelling is performed, in a leave-one-out scheme, for each case of a publicly available training set, which consists of 30 real cases (20 high-grade gliomas, 10 low-grade gliomas) and 50 synthetic cases (25 high-grade gliomas, 25 low-grade gliomas). Promising results are shown on the training set, and we believe this algorithm would perform favourably well in comparison to the state of the art on a testing set.
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Contributor : Nicolas Cordier Connect in order to contact the contributor
Submitted on : Monday, December 16, 2013 - 4:49:30 PM
Last modification on : Saturday, June 25, 2022 - 11:12:02 PM
Long-term archiving on: : Tuesday, March 18, 2014 - 12:25:47 PM


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  • HAL Id : hal-00917084, version 1



Nicolas Cordier, Bjoern Menze, Hervé Delingette, Nicholas Ayache. Patch-based Segmentation of Brain Tissues. MICCAI Challenge on Multimodal Brain Tumor Segmentation, Sep 2013, Nagoya, Japan. pp.6 - 17. ⟨hal-00917084⟩



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