Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool

Eelke Visser, Max C. Keuken, Gwenaëlle Douaud, Veronique Gaura, Anne Catherine Bachoud-Levi, Philippe Remy, Birte U. Forstmann, Mark Jenkinson

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Accurate segmentation of the subcortical structures is frequently required in neuroimaging studies. Most existing methods use only a T1-weighted MRI volume to segment all supported structures and usually rely on a database of training data. We propose a new method that can use multiple image modalities simultaneously and a single reference segmentation for initialisation, without the need for a manually labelled training set. The method models intensity profiles in multiple images around the boundaries of the structure after nonlinear registration. It is trained using a set of unlabelled training data, which may be the same images that are to be segmented, and it can automatically infer the location of the physical boundary using user-specified priors. We show that the method produces high-quality segmentations of the striatum, which is clearly visible on T1-weighted scans, and the globus pallidus, which has poor contrast on such scans. The method compares favourably to existing methods, showing greater overlap with manual segmentations and better consistency.

Original languageEnglish
Pages (from-to)479-497
Number of pages19
JournalNeuroImage
Volume125
DOIs
Publication statusPublished - 15 Jan 2016
Externally publishedYes

Keywords

  • Brain
  • Globus pallidus
  • Huntington
  • Multimodal
  • Segmentation
  • Striatum

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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