Spatial decision forests for MS lesion segmentation in multi-channel MR images

Authors:
Ezequiel Geremia;Bjoern H. Menze;Olivier Clatz;Ender Konukoglu;Antonio Criminisi;Nicholas Ayache
Affiliations:
INRIA Sophia-Antipolis, France and Machine Learning and Perception Group, Microsoft Research Cambridge, UK;INRIA Sophia-Antipolis, France and Computer Science and Artificial Intelligence Laboratory, MIT;INRIA Sophia-Antipolis, France;Microsoft Research Cambridge, UK;Microsoft Research Cambridge, UK;INRIA Sophia-Antipolis, France
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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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 MR 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.