Discriminative, Semantic Segmentation of Brain Tissue in MR Images

Authors:
Zhao Yi;Antonio Criminisi;Jamie Shotton;Andrew Blake
Affiliations:
University of California, Los Angeles, USA;Microsoft Research Cambridge, UK;Microsoft Research Cambridge, UK;Microsoft Research Cambridge, UK
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

A new algorithm is presented for the automatic segmentation and classification of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classification and takes into account partial volume effects. This is combined with correction of intensities for the MR bias field, in conjunction with a learned model of spatial context, to achieve accurate voxel-wise classification. Our quantitative validation, carried out on existing labelled datasets, demonstrates improved results over the state of the art, especially for the cerebro-spinal fluid class which is the most difficult to label accurately.