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In this paper, we propose a novel learning based method for automatedsegmenta-tion of brain tumor in multimodal MRI images. The machine learnedfeatures from fully convolutional neural network (FCN) and hand-designed textonfea-tures are used to classify the MRI image voxels. The score map withpixel-wise predictions is used as a feature map which is learned frommultimodal MRI train-ing dataset using the FCN. The learned features are thenapplied to random for-ests to classify each MRI image voxel into normal braintissues and different parts of tumor. The method was evaluated on BRATS 2013challenge dataset. The results show that the application of the random forestclassifier to multimodal MRI images using machine-learned features based on FCNand hand-designed features based on textons provides promising segmentations.The Dice overlap measure for automatic brain tumor segmentation against groundtruth is 0.88, 080 and 0.73 for complete tumor, core and enhancing tumor,respectively.

Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network