Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool.
Web of Science
AuthorVisser, E; Keuken, MC; Douaud, G; Gaura, V; Bachoud-Levi, A-C; Remy, P; Forstmann, BU; Jenkinson, M
University of Melbourne Author/sJenkinson, Mark
AffiliationCentre for Neuroscience
Document TypeJournal Article
CitationsVisser, E., Keuken, M. C., Douaud, G., Gaura, V., Bachoud-Levi, A. -C., Remy, P., Forstmann, B. U. & Jenkinson, M. (2016). Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool.. Neuroimage, 125, pp.479-497. https://doi.org/10.1016/j.neuroimage.2015.10.013.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692519
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.
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