A large margin algorithm for automated segmentation of white matter hyperintensity

Qin, C., Guerrero, R., Bowles, C., Chen, L., Dickie, D. A. , Valdes-Hernandez, M. d. C., Wardlaw, J. and Rueckert, D. (2018) A large margin algorithm for automated segmentation of white matter hyperintensity. Pattern Recognition, 77, pp. 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.

• Item Type: Articles
• Additional Information: C. Qin is supported by China Scholarship Council (CSC). The generation of the reference data received funds from Age UK with additional funding from the UK Medical Research Council [Grant numbers G0701120, G1001245 and MR/M013111/1]. Magnetic Resonance Image acquisition and analyses were conducted at the Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh (www.bric.ed.ac.uk) which is part of SINAPSE (Scottish Imaging NetworkA Platform for Scientific Excellence) collaboration (www.sinapse.ac.uk) funded by the Scottish Funding Council and the Chief Scientist Office. Support from the Fondation Leducq Network for the Study of Perivascular Spaces in Small Vessel Disease [ref no. 16 CVD 05] and European Union Horizon 2020, PHC-03-15, [project No 666881, ‘SVDs@Target’].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dickie, Dr David Alexander
• Authors: Qin, C., Guerrero, R., Bowles, C., Chen, L., Dickie, D. A., Valdes-Hernandez, M. d. C., Wardlaw, J., and Rueckert, D.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Journal Name: Pattern Recognition
• Publisher: Elsevier
• ISSN: 0031-3203
• ISSN (Online): 1873-5142
• Published Online: 18 December 2017
• Copyright Holders: Copyright © 2017 Elsevier Ltd.
• First Published: First published in Pattern Recognition 77:150-159
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher