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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Communication dans un congrès
MICCAI Challenge on Multimodal Brain Tumor Segmentation, 2012, Nice, France. 2012, MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation
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https://hal.inria.fr/hal-00813827
Contributeur : Project-Team Asclepios <>
Soumis le : mardi 16 avril 2013 - 11:16:25
Dernière modification le : jeudi 11 janvier 2018 - 16:24:56

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  • HAL Id : hal-00813827, version 1

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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. 2012, MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation. 〈hal-00813827〉

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