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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 publication

DOI

10.1016/j.neuroimage.2015.10.013

Type

Journal article

Journal

Neuroimage

Publication Date

15/01/2016

Volume

125

Pages

479 - 497

Keywords

Brain, Globus pallidus, Huntington, Multimodal, Segmentation, Striatum, Adult, Algorithms, Brain Mapping, Corpus Striatum, Female, Globus Pallidus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Neuronavigation, Pattern Recognition, Automated