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Bio-Inspired Segmentation and Detection Methods for Human Embryonic Stem Cells

  • Benjamin X. GuanEmail author
  • Bir Bhanu
  • Prue Talbot
  • Nikki Jo-Hao Weng
Chapter
Part of the Computational Biology book series (COBO, volume 22)

Abstract

This paper is a review on the bio-inspired human embryonic stem cell (hESC) segmentation and detection methods. Five different morphological types of hESC have been identified: (1) unattached; (2) substrate-attached; (3) dynamically blebbing; (4) apoptotically blebbing; and (5) apoptotic. Each type has distinguishing image properties. Within each type, cells are also different in size and shape. Three automatic approaches for hESC region segmentation and one method for unattached stem cell detection are introduced to assist biologists in analysis of hESC cell health and for application in drug testing and toxicological studies.

Keywords

Cell Region Human Embryonic Stem Cell Average Sensitivity Gradient Magnitude Weighted Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This research was supported by National Science Foundation-Integrated Graduate Education Research and Training (NSF-IGERT): Video Bioinformatics Grant DGE 0903667 and by Tobacco-Related Disease Research Program (TRDRP): Grant 20XT-0118.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benjamin X. Guan
    • 1
    Email author
  • Bir Bhanu
    • 1
  • Prue Talbot
    • 2
  • Nikki Jo-Hao Weng
    • 3
  1. 1.Center for Research in Intelligent SystemsUniversity of CaliforniaRiversideUSA
  2. 2.Department of Cell Biology and NeuroscienceUniversity of CaliforniaRiversideUSA
  3. 3.Department of Cell Biology and NeuroscienceUniversity of CaliforniaRiversideUSA

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