Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning.

Journal: Fertility and sterility
PMID: 35562203

Abstract

OBJECTIVE: To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients.

Authors

  • Ryan Lee
  • Luke Witherspoon
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, The Ottawa Hospital, Ottawa, Ontario, Canada.
  • Meghan Robinson
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada.
  • Jeong Hyun Lee
    1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.
  • Simon P Duffy
    Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
  • Ryan Flannigan
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, Weill Cornell Medicine, New York, New York. Electronic address: ryan.flannigan@ubc.ca.
  • Hongshen Ma
    Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada. hongma@mech.ubc.ca.

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