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A large margin algorithm for automated segmentation of white matter hyperintensity

Qin C, Guerrero R, Bowles C, Chen L, Dickie DA, Valdes-Hernandez MDC, Wardlaw J, Rueckert D (2018)

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Publication Type: Journal article

Publication year: 2018

Journal

Book Volume: 77

Pages Range: 150-159

DOI: 10.1016/j.patcog.2017.12.016

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.

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How to cite

APA:

Qin, C., Guerrero, R., Bowles, C., Chen, L., Dickie, D.A., Valdes-Hernandez, M.D.C.,... Rueckert, D. (2018). A large margin algorithm for automated segmentation of white matter hyperintensity. Pattern Recognition, 77, 150-159. https://doi.org/10.1016/j.patcog.2017.12.016

MLA:

Qin, Chen, et al. "A large margin algorithm for automated segmentation of white matter hyperintensity." Pattern Recognition 77 (2018): 150-159.

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