Abstract:
:Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimer's, Huntington's, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10(-3)) and also increased thickness estimation accuracy when compared to three well established techniques.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Cardoso MJ,Clarkson MJ,Ridgway GR,Modat M,Fox NC,Ourselin S,Alzheimer's Disease Neuroimaging Initiative.doi
10.1016/j.neuroimage.2011.02.013subject
Has Abstractpub_date
2011-06-01 00:00:00pages
1386-97issue
3eissn
1053-8119issn
1095-9572pii
S1053-8119(11)00152-2journal_volume
56pub_type
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