Learning-based segmentation framework for tissue images containing gene expression data

IEEE Trans Med Imaging. 2007 May;26(5):728-44. doi: 10.1109/TMI.2007.895462.


Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the more than 20 000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, is being used to create a searchable database of gene expression patterns in the adult mouse brain. Our framework annotates the images about four times faster and has achieved a median spatial overlap of up to 0.92 compared with expert segmentation in 64 images tested. This tool is intended to help scientists interpret large-scale gene expression patterns more efficiently.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Animals
  • Artificial Intelligence*
  • Brain / cytology*
  • Brain / metabolism*
  • Gene Expression Profiling / methods*
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods
  • Mice
  • Mice, Inbred C57BL
  • Nerve Tissue Proteins / metabolism*
  • Pattern Recognition, Automated / methods
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Tissue Distribution


  • Nerve Tissue Proteins