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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 de- veloped 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, reli- able 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 ac- curacy of automated atlas-deformation. The resulting large-scale annotation will help scientists interpret gene expression patterns more rapidly and accurately.

Landmark-Driven, Atlas-Based Segmentation of Mouse Brain Tissue Images Containing Gene Expression Data