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  Landmark-driven, atlas-based segmentation of mouse brain tissue images containing gene expression data.

Kakadiaris, I., Bello, M., Arunachalam, S., Kang, W., Ju, T., Warren, J., et al. (2004). Landmark-driven, atlas-based segmentation of mouse brain tissue images containing gene expression data. In Medical Image omuting and Computer-Assisted Intervention (pp. 192-199).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-238E-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-C4D9-D
Genre: Book Chapter

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 Creators:
Kakadiaris, I., Author
Bello, M., Author
Arunachalam, S., Author
Kang, W., Author
Ju, T., Author
Warren, J., Author
Carson, J., Author
Chiu, W., Author
Thaller, C., Author
Eichele, G.1, Author              
Affiliations:
1Department of Molecular Embryology, Max Planck Institute for Experimental Endocrinology, Max Planck Society, ou_1565140              

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 Abstract: To better understand the development and function of the mammalian brain, researchers have begun to systematically collect a large number of gene expression patterns throughout the mouse brain using technology recently developed for this task. Associating specific gene activity with specific functional locations in the brain anatomy results in a greater understanding of the role of the gene's products. To perform such an association for a large amount of data, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we present an anatomical landmark detection method that has been incorporated into an atlas-based segmentation. The addition of this technique significantly increases the accuracy of automated atlas-deformation. The resulting large-scale annotation will help scientists interpret gene expression patterns more rapidly and accurately.

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Language(s): eng - English
 Dates: 2004
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION
 Rev. Method: -
 Identifiers: -
 Degree: -

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Title: Medical Image omuting and Computer-Assisted Intervention
Source Genre: Book
 Creator(s):
Barrilot, C., Author
Haynor, D. L., Author
Hellier, P., Author
Affiliations:
-
Publ. Info: -
Pages: - Volume / Issue: 3216 Sequence Number: - Start / End Page: 192 - 199 Identifier: -