Edinburgh Research Explorer

A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation

Research output: Chapter in Book/Report/Conference proceedingChapter

  • Chen Qin
  • Ricardo Guerrero Moreno
  • Christopher Bowles
  • Christian Ledig
  • Philip Scheltens
  • Frederik Barkhof
  • Hanneke Rhodius-Meester
  • Betty Tijms
  • Afina W. Lemstra
  • Wiesje M. van der Flier
  • Ben Glocker
  • Daniel Rueckert

Related Edinburgh Organisations

Original languageEnglish
Title of host publicationMACHINE LEARNING IN MEDICAL IMAGING, MLMI 2016
Pages104-112
Volume10019
DOIs
Publication statusPublished - 1 Oct 2016

Publication series

NameLecture Notes in Computer Science

Abstract

Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation.

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