Combining Unsupervised and Supervised Methods for Lesion Segmentation

Abstract

White-matter lesions are associated to several diseases, which can be characterized by neuroimaging biomarkers through lesion segmentation in MR images. We present a novel automated lesion segmentation method consisting of an unsupervised mixture model based extraction of candidate lesion voxels, which are subsequently classified by a random decision forest (RDF) using simple visual features like multi-sequence MR intensities sourced from connected voxel neighborhoods. The candidate lesion extraction prior to RDF training and classification balanced the number of non-lesion and lesion voxels and the number of non-lesion classes versus a lesion class. Thereby, the RDF established highly discriminating decision rules based on such simple visual features, which have the benefit of no computational overhead and easy extraction from the MR images. On MR images of 18 patients with multiple sclerosis the proposed method achieved the median Dice similarity of 0.73, sensitivity of 0.90 and positive predictive value of 0.61, which indicate accurate segmentation of white-matter lesions.