@Inproceedings {export:172241, abstract = {  We describe our
submission to the Brain Tumor Segmentation Challenge (BraTS) at
MICCAI 2012, which is based on our method for tissue-specific
segmentation of high-grade brain tumors [3].  The main idea is
to cast the segmentation as a classification task, and use the
discriminative power of context information. We realize this
idea by equipping a classification forest (CF) with spatially
non-local features to represent the data, and by providing the
CF with initial probability estimates for the single tissue
classes as additional input (along-side the MRI channels). The
initial probabilities are patient-specific, and computed at test
time based on a learned model of intensity. Through the
combination of the initial probabilities and the non-local
features, our approach is able to capture the context
information for each data point.  Our method is fully automatic,
with segmentation run times in the range of 1-2 minutes per
patient. We evaluate the submission by cros