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

Neuroimage. 2016 Jan 15;125:479-497. doi: 10.1016/j.neuroimage.2015.10.013. Epub 2015 Oct 19.

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.

Keywords: Brain; Globus pallidus; Huntington; Multimodal; Segmentation; Striatum.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Brain Mapping / methods*
  • Corpus Striatum / anatomy & histology*
  • Female
  • Globus Pallidus / anatomy & histology*
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods
  • Male
  • Models, Neurological*
  • Neuronavigation / methods
  • Pattern Recognition, Automated / methods