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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Visser E,Keuken MC,Douaud G,Gaura V,Bachoud-Levi AC,Remy P,Forstmann BU,Jenkinson Mdoi
10.1016/j.neuroimage.2015.10.013subject
Has Abstractpub_date
2016-01-15 00:00:00pages
479-497eissn
1053-8119issn
1095-9572pii
S1053-8119(15)00914-3journal_volume
125pub_type
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