Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Random Forest Approach

The development of automatic tumor detection and segmentation procedures enables the computers to preprocess huge sets of MRI records and draw the attention of medical staff upon suspected positive cases. This paper proposes a machine learning solution based on binary decision trees and random forest technique, trained to provide accurate segmentation of brain tumors from multispectral MRI volumes. The current version of our system was trained and tested using all 220 high-grade tumor volumes from the MICCAI BRATS 2016 database. Image records were preprocessed to attenuate the effect of relative intensities in the MRI data, and to extend the feature set with neighborhood information of each voxel. The output of the random forest is also validated for each voxel, according to labels given to neighbor voxels. The achieved accuracy is characterized by an overall mean Dice score of 80.1%, sensitivity 83.1%, and specificity 98.6%. The proposed method is likely to detect all gliomas of 2 cm diameter.