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Proceedings Paper

Large scale digital atlases in neuroscience
Author(s): M. Hawrylycz; D. Feng; C. Lau; C. Kuan; J. Miller; C. Dang; L. Ng
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Paper Abstract

Imaging in neuroscience has revolutionized our current understanding of brain structure, architecture and increasingly its function. Many characteristics of morphology, cell type, and neuronal circuitry have been elucidated through methods of neuroimaging. Combining this data in a meaningful, standardized, and accessible manner is the scope and goal of the digital brain atlas. Digital brain atlases are used today in neuroscience to characterize the spatial organization of neuronal structures, for planning and guidance during neurosurgery, and as a reference for interpreting other data modalities such as gene expression and connectivity data. The field of digital atlases is extensive and in addition to atlases of the human includes high quality brain atlases of the mouse, rat, rhesus macaque, and other model organisms. Using techniques based on histology, structural and functional magnetic resonance imaging as well as gene expression data, modern digital atlases use probabilistic and multimodal techniques, as well as sophisticated visualization software to form an integrated product. Toward this goal, brain atlases form a common coordinate framework for summarizing, accessing, and organizing this knowledge and will undoubtedly remain a key technology in neuroscience in the future. Since the development of its flagship project of a genome wide image-based atlas of the mouse brain, the Allen Institute for Brain Science has used imaging as a primary data modality for many of its large scale atlas projects. We present an overview of Allen Institute digital atlases in neuroscience, with a focus on the challenges and opportunities for image processing and computation.

Paper Details

Date Published: 21 March 2014
PDF: 12 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340Z (21 March 2014); doi: 10.1117/12.2044203
Show Author Affiliations
M. Hawrylycz, Allen Institute for Brain Science (United States)
D. Feng, Allen Institute for Brain Science (United States)
C. Lau, Allen Institute for Brain Science (United States)
C. Kuan, Allen Institute for Brain Science (United States)
J. Miller, Allen Institute for Brain Science (United States)
C. Dang, Allen Institute for Brain Science (United States)
L. Ng, Allen Institute for Brain Science (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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