A large margin algorithm for automated segmentation of white matter hyperintensity

Chen Qin, Ricardo Guerrero, Christopher Bowles, Liang Chen, David Alexander Dickie, Maria del C. Valdes-Hernandez, Joanna Wardlaw, Daniel Rueckert

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)150-159
Number of pages10
JournalPattern Recognition
Early online date18 Dec 2017
Publication statusPublished - 31 May 2018

Keywords / Materials (for Non-textual outputs)

  • Supervised learning
  • Semi-supervised learning
  • Segmentation
  • White matter hyperintensity
  • Brain MRI


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