Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images
Lecture notes in computer sciencePublished 1 January 2010
Ezequiel Geremia, Bjoern Menze, Olivier Clatz, Ender Konukoğlu, Antonio Criminisi, Nicholas Ayache
Citations97
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A new algorithm is presented for the automatic segmentation of Multiple Sclerosis lesions in 3D MR images that builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume.
Abstract
A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MIR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.
Keywords
Computer ScienceNeuroscienceBiochemistry, Genetics and Molecular Biology
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