ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual Clustering

Abstract

Automated methods for segmentation of ischemic stroke lesions could significantly reduce the workload of radiologists and speed up the beginning of patient treatment. In this paper, we present a method for subacute ischemic stroke lesion segmentation from multispectral magnetic resonance images (MRI). The method involves classification of voxels with a Random Forest algorithm and subsequent classification refinement with contextual clustering. In addition, we utilize the training data to build statistical group-specific templates and use them for calculation of individual voxel-wise differences from the global mean. Our method achieved a Dice coefficient of 0.61 for the leave-one-out cross-validated training data and 0.47 for the testing data of the ISLES challenge 2015.