%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2020/09.28.12.07
%2 sid.inpe.br/sibgrapi/2020/09.28.12.07.19
%@doi 10.1109/SIBGRAPI51738.2020.00032
%T Detecting Alzheimer’s Disease based on Structural Region Analysis using a 3D Shape Descriptor
%D 2020
%A Duarte, Kauê Tartarotti Nepomuceno,
%A Gobbi, David,
%A Frayne, Richard,
%A Carvalho, Marco Antonio Garcia de,
%@affiliation School of Technology, University of Campinas
%@affiliation Calgary Image Processing and Analysis Centre, Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary
%@affiliation Calgary Image Processing and Analysis Centre, Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary
%@affiliation School of Technology, University of Campinas
%E Musse, Soraia Raupp,
%E Cesar Junior, Roberto Marcondes,
%E Pelechano, Nuria,
%E Wang, Zhangyang (Atlas),
%B Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)
%C Porto de Galinhas (virtual)
%8 7-10 Nov. 2020
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K shape descriptor, alzheimer, image segmentation, similarity, brain analysis.
%X Alzheimers disease (AD) is a common neurodegenerative dementia that affects older people. Changes in behavior and cognition are the most common characteristics of this disease and are associated with changes in brain structure. Techniques focusing on brain shape have been recently proposed to quantify and understand these changes. One challenge when examining AD is that each anatomical region may have a unique role in and time course for brain deterioration, requiring a whole-brain method that is capable of individual (or regional) analyses at different disease stages. We propose to apply the scale-invariant heat kernel signature descriptor to magnetic resonance brain images in order to evaluate regional shape features across different brain regions. We measured the shape feature similarity in 500 subjects, equally divided across five progressive, disease-based stages. The shape analysis provided a complementary perspective to whole-brain analysis, due to the capability of identifying how different structures degenerate at different rates in the brain. In total, a group of 99 distinct brain regions belonging to cortical and deep gray matter were analyzed across the five disease stages. Preliminary assessment of shape-based analysis of key brain regions demonstrated that SIHKS was predictive of disease stage and disease progression.
%@language en
%3 Sibgrapi_2020___Kaue_TND_CAMERAREADY.pdf