publication . Article . Other literature type . 2012

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease.

Simon Eskildsen; Vladimir Fonov; Pierrick Coupe; D. Louis Collins;
Open Access English
  • Published: 15 Feb 2012
  • Publisher: Elsevier
  • Country: Spain
Abstract
Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-bas...
Subjects
free text keywords: Hippocampus, Hippocampus volume, Hippocampus grading, Patient's classification, Nonlocal means estimator, Alzheimer's disease, Entorhinal cortex, FISICA APLICADA, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Time Factors, medial temporal lobe, Aging, detection, segmentation, grading, brain, neuroimaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Cognitive Neuroscience, Neurology
Related Organizations
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
Communities
Neuroinformatics
64 references, page 1 of 5

Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D., 2009. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46, 726-738. [OpenAIRE]

Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry--the methods. Neuroimage 11, 805-821.

Barnes, J., Foster, J., Boyes, R.G., Pepple, T., Moore, E.K., Schott, J.M., Frost, C., Scahill, R.I., Fox, N.C., 2008. A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus. Neuroimage 40, 1655-1671. [OpenAIRE]

Bishop, C.A., Jenkinson, M., Andersson, J., Declerck, J., Merhof, D., 2011. Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): method and validation on clinical data. Neuroimage 55, 1009-1019. [OpenAIRE]

Black, S.E., 1999. The search for diagnostic and progression markers in AD: so near but still too far? Neurology 52, 1533-1534.

Brox, T., Kleinschmidt, O., Cremers, D., 2008. Efficient nonlocal means for denoising of textural patterns. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 17, 1083-1092. [OpenAIRE]

Buades, A., Coll, B., Morel, J.M., 2005. A non-local algorithm for image denoising. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol 2, Proceedings, 60-65. [OpenAIRE]

Buades, A., Coll, B., Morel, J.M., 2010. Image Denoising Methods. A New Nonlocal Principle. Siam Review 52, 113- 147. [OpenAIRE]

Chupin, M., Gerardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehericy, S., Benali, H., Garnero, L., Colliot, O., 2009a. Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19, 579-587. [OpenAIRE]

Chupin, M., Hammers, A., Liu, R.S., Colliot, O., Burdett, J., Bardinet, E., Duncan, J.S., Garnero, L., Lemieux, L., 2009b. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage 46, 749-761. [OpenAIRE]

Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C., 1994. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18, 192-205.

Collins, D.L., Pruessner, J.C., 2010. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. Neuroimage 52, 1355-1366. [OpenAIRE]

Colliot, O., Chetelat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., Dubois, B., Garnero, L., Eustache, F., Lehericy, S., 2008. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248, 194-201. [OpenAIRE]

Copenhaver, B.R., Rabin, L.A., Saykin, A.J., Roth, R.M., Wishart, H.A., Flashman, L.A., Santulli, R.B., McHugh, T.L., Mamourian, A.C., 2006. The fornix and mammillary bodies in older adults with Alzheimer's disease, mild cognitive impairment, and cognitive complaints: a volumetric MRI study. Psychiatry Res 147, 93-103. [OpenAIRE]

Coupé, P., Fonov, V., Eskildsen, S., Manjon, J., Arnold, D., Collins, L., 2011a. Influence of the training library composition on a patch-based label fusion method: Application to hippocampus segmentation on the ADNI dataset. [OpenAIRE]

64 references, page 1 of 5
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue