ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images

Abstract

We present a novel fully-automated generative ischemic stroke lesion segmentation method that can be applied to individual patient images without need for a training data set. An Expectation Maximization-approach is used for estimating intensity models for both normal and pathological tissue. The segmentation is represented by a level-set that is iteratively updated to label voxels as either normal or pathological, based on which intensity model explains the voxels’ intensity the best. A convex level-set formulation is adopted, that eliminates the need for manual initialization of the level-set. The performance of the method for segmenting the ischemic stroke is summarized by an average Dice score of $0.78\pm 0.08$ and $0.53 \pm 0.26$ for the SPES and SISS 2015 training data set respectively and $0.67\pm 0.24$ and $0.37 \pm 0.33$ for the test data set.