A large margin algorithm for automated segmentation of white matter hyperintensity. (May 2018) - Qin, Chen; Guerrero, Ricardo; Bowles, Christopher; Chen, Liang; Dickie, David Alexander; Valdes-Hernandez, Maria del C.; Wardlaw, Joanna; Rueckert, Daniel
Highlights: A novel large margin method for white matter hyperintensity segmentation is proposed. A supervised large margin algorithm is proposed to learn a global classifier. A semi-supervised large margin classifier is learned for refinement on test subject. The proposed model shows competitive performance on subjects with vascular disease. Abstract: Precise detection and quantification of white matter hyperintensity (WMH) is of great interest in studies of neurological and vascular disorders. In this work, we propose a novel method for automatic WMH segmentation with both supervised and semi-supervised large margin algorithms provided by the framework. The proposed algorithms optimize a kernel based max-margin objective function which aims to maximize the margin between inliers and outliers. We show that the semi-supervised learning problem can be formulated to learn a classifier and label assignment simultaneously, which can be solved efficiently by an iterative algorithm. The model is learned first via the supervised approach and then fine-tuned on a target image by using the semi-supervised algorithm. We evaluate our method on 88 brain fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images from subjects with vascular disease. Quantitative evaluation of the proposed approach shows that it outperforms other well known methods for WMH segmentation.