Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping

Brian Canada, Georgia Thomas, Keith Cheng, James Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Because of its small size and rapid development, the larval zebrafish is an ideal model organism for studying mutant phenotypes using "high- throughput" histological analysis. Although the preparation and subsequent digitization of zebrafish larval histology specimens can be conducted in parallel, the scoring and annotation of the resulting virtual slides is largely manual and therefore rate limiting, which motivates the development of systems for automated characterization of histology images. We present a prototype for automated segmentation and classification of histology images in animal models, with a pilot study focusing on larval zebrafish eye and gut images. We show that the segmentation of the images into regions of individual cell layers can be conducted with good precision using combinations of widely-used image processing operations, and that the resulting classification system, based on a decision tree algorithm, exhibits promising performance.

Original languageEnglish (US)
Title of host publication2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA
Pages245-248
Number of pages4
DOIs
StatePublished - Sep 8 2008
Event2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA - Bethesda, MD, United States
Duration: Nov 8 2007Nov 9 2007

Other

Other2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA
CountryUnited States
CityBethesda, MD
Period11/8/0711/9/07

Fingerprint

Histology
Throughput
Analog to digital conversion
Decision trees
Animals
Image processing
Cells

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

Cite this

Canada, B., Thomas, G., Cheng, K., & Wang, J. (2008). Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping. In 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA (pp. 245-248). [4400930] https://doi.org/10.1109/LSSA.2007.4400930
Canada, Brian ; Thomas, Georgia ; Cheng, Keith ; Wang, James. / Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping. 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA. 2008. pp. 245-248
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Canada, B, Thomas, G, Cheng, K & Wang, J 2008, Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping. in 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA., 4400930, pp. 245-248, 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA, Bethesda, MD, United States, 11/8/07. https://doi.org/10.1109/LSSA.2007.4400930

Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping. / Canada, Brian; Thomas, Georgia; Cheng, Keith; Wang, James.

2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA. 2008. p. 245-248 4400930.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Canada B, Thomas G, Cheng K, Wang J. Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping. In 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA. 2008. p. 245-248. 4400930 https://doi.org/10.1109/LSSA.2007.4400930