Cytopathic Effect Detection and Clonal Selection using Deep Learning.

Journal: Pharmaceutical research
PMID:

Abstract

PURPOSE: In biotechnology, microscopic cell imaging is often used to identify and analyze cell morphology and cell state for a variety of applications. For example, microscopy can be used to detect the presence of cytopathic effects (CPE) in cell culture samples to determine virus contamination. Another application of microscopy is to verify clonality during cell line development. Conventionally, inspection of these microscopy images is performed manually by human analysts. This is both tedious and time consuming. In this paper, we propose using supervised deep learning algorithms to automate the cell detection processes mentioned above.

Authors

  • Yu Yuan
    Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
  • Tony Wang
    Imedacs, Ann Arbor, MI.
  • Jordan Sims
    Amgen, Inc., Thousand Oaks, 91320, CA, USA.
  • Kim Le
    Health Sciences Centre, Ontario Veterinary College, Guelph, Ontario, Canada, Current address: Sydney Exotics and Rabbit Vets, North Shore Veterinary Specialist Hospital, Sydney, Australia.
  • Cenk Ündey
    Digital Integration and Predictive Technologies, Amgen, Inc., Thousand Oaks, California.
  • Erdal Oruklu
    Illinois Institute of Technology, Chicago, 60616, IL, USA. oruklu@iit.edu.