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

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.