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Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images

Abstract : A fully automatic algorithm is presented for the automatic segmentation of gliomas in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classi cation of the volume. Our method uses multi-channel MR intensi- ties (T1, T1C, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced ac- counting for the fact that gliomas tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the BRATS Segmentation Challenge 2012 dataset and demonstrates improved results over the state of the art.
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Submitted on : Tuesday, April 16, 2013 - 11:16:25 AM
Last modification on : Saturday, June 25, 2022 - 11:09:56 PM


  • HAL Id : hal-00813827, version 1



Ezequiel Geremia, Bjoern H. Menze, Nicholas Ayache. Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images. MICCAI Challenge on Multimodal Brain Tumor Segmentation, 2012, Nice, France. ⟨hal-00813827⟩



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