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

Musodiq Bello, Tao Ju, James Carson, Joe Warren, Wah Chiu, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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 20000 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.

Original languageEnglish (US)
Pages (from-to)728-744
Number of pages17
JournalIEEE Transactions on Medical Imaging
Issue number5
StatePublished - May 2007


  • Feature selection
  • Gene expression
  • Segmentation
  • Shape modeling
  • Texture classification

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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