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 crossvalidation on the real and synthetic, highand low-grade tumor BraTS data sets.
[1]
Anil K. Jain,et al.
Unsupervised texture segmentation using Gabor filters
,
1990,
1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.
[2]
Jean Ponce,et al.
Learning mid-level features for recognition
,
2010,
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[3]
Simon J. Doran,et al.
Autoencoder in Time-Series Analysis for Unsupervised Tissues Characterisation in a Large Unlabelled Medical Image Dataset
,
2011,
2011 10th International Conference on Machine Learning and Applications and Workshops.
[4]
Yihong Gong,et al.
Linear spatial pyramid matching using sparse coding for image classification
,
2009,
CVPR.