DocumentCode :
2153485
Title :
Disease-specific probabilistic brain atlases
Author :
Thompson, Paul ; Mega, Michael S. ; Toga, Arthur W.
Author_Institution :
Lab. of Neuro Imaging, California Univ., Los Angeles, CA, USA
fYear :
2000
fDate :
2000
Firstpage :
227
Lastpage :
234
Abstract :
Atlases of the human brain, in health and disease, provide a comprehensive framework for understanding brain structure and function. The complexity and variability of brain structure, especially in the gyral patterns of the human cortex, present challenges in creating standardized brain atlases that reflect the anatomy of a population. This paper introduces the concept of a population-based, disease-specific brain atlas that can reflect the unique anatomy and physiology of a particular clinical subpopulation. Based on well-characterized patient groups, disease-specific atlases contain thousands of structure models, composite maps, average templates, and visualizations of structural variability, asymmetry and group-specific differences. They correlate the structural, metabolic, molecular and histologic hallmarks of the disease. Rather than simply fusing information from multiple subjects and sources, new mathematical strategies are introduced to resolve group-specific features not apparent in individual scans. High-dimensional elastic mappings, based on covariant partial differential equations, are developed to encode patterns of cortical variation. In the resulting brain atlas, disease-specific features and regional asymmetries emerge that are not apparent in individual anatomies. The resulting probabilistic atlas can identify patterns of altered structure and function, and can guide algorithms for knowledge-based image analysis, automated image labeling, tissue classification, data mining and functional image analysis
Keywords :
biomedical MRI; brain; data mining; diseases; image classification; image coding; medical image processing; partial differential equations; probability; automated image labeling; brain MRI; brain function; brain structure; clinical subpopulation; complexity; cortical variation patterns; covariant partial differential equations; disease-specific probabilistic brain atlases; functional image analysis; health; high-dimensional elastic mappings; individual scans; knowledge-based image analysis; mathematical strategies; tissue classification; variability; well-characterized patient groups; Anatomy; Brain modeling; Data mining; Diseases; Humans; Image analysis; Labeling; Partial differential equations; Physiology; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mathematical Methods in Biomedical Image Analysis, 2000. Proceedings. IEEE Workshop on
Conference_Location :
Hilton Head Island, SC
Print_ISBN :
0-7695-0737-9
Type :
conf
DOI :
10.1109/MMBIA.2000.852382
Filename :
852382
Link To Document :
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