An imaging workflow for characterizing phenotypical change in large histological mouse model datasets

Kishore Mosaliganti, Tony Pan, Randall Ridgway, Richard Sharp, Lee Alex Donald Cooper, Alex Gulacy, Ashish Sharma, Okan Irfanoglu, Raghu Machiraju, Tahsin Kurc, Alain de Bruin, Pamela Wenzel, Gustavo Leone, Joel Saltz, Kun Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Motivation: This paper presents a workflow designed to quantitatively characterize the 3D structural attributes of macroscopic tissue specimens acquired at a micron level resolution using light microscopy. The specific application is a study of the morphological change in a mouse placenta induced by knocking out the retinoblastoma gene. Result: This workflow includes four major components: (i) serial section image acquisition, (ii) image preprocessing, (iii) image analysis involving 2D pair-wise registration, 2D segmentation and 3D reconstruction, and (iv) visualization and quantification of phenotyping parameters. Several new algorithms have been developed within each workflow component. The results confirm the hypotheses that (i) the volume of labyrinth tissue decreases in mutant mice with the retinoblastoma (Rb) gene knockout and (ii) there is more interdigitation at the surface between the labyrinth and spongiotrophoblast tissues in mutant placenta. Additional confidence stem from agreement in the 3D visualization and the quantitative results generated. Availability: The source code is available upon request.

Original languageEnglish (US)
Pages (from-to)863-873
Number of pages11
JournalJournal of Biomedical Informatics
Issue number6
StatePublished - Dec 2008


  • Genetic phenotyping
  • Histology staining
  • Image analysis
  • Image registration
  • Imaging workflow
  • Light microscopy
  • Morphometrics
  • Mutation
  • Segmentation
  • Visualization

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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