The publication detail shows the title, authors (with indicators showing other profiled authors), information on the publishing organization, abstract and a link to the article in PubMed. This abstract is what is used to create the fingerprint of the publication. If any grants are referenced by the publication, they will be listed here as well.
LoAd: a locally adaptive cortical segmentation algorithm.
M Jorge Cardoso; Matthew J Clarkson; Gerard R Ridgway; Marc Modat; Nick C Fox; Sebastien Ourselin; (Profiled Author: Fox, Nick C)
Centre for Medical Image Computing (CMIC), University College London, London, UK. email@example.com
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.
1 Originating Grant
Jagust, William J
30 September 1991 - 30 June 2006
NATIONAL INSTITUTE ON AGING
Total Funding: $ 12,148,407
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