Deep learning neural network analysis of human blastocyst expansion from time-lapse image files.

Journal: Reproductive biomedicine online
Published Date: Jun 1, 2021

Abstract

RESEARCH QUESTION: Can artificial intelligence (AI) discriminate a blastocyst's cellular area from unedited time-lapse image files using semantic segmentation and a deep learning optimized U-Net architecture for use in selecting single blastocysts for transfer?

Authors

  • Thomas T F Huang
    Pacific In Vitro Fertilization Institute and John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA. Electronic address: huangt@hawaii.edu.
  • Thomas Kosasa
    Pacific In Vitro Fertilization Institute and John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
  • Brienne Walker
    Pacific In Vitro Fertilization Institute and John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
  • Christina Arnett
    Advanced Reproductive Center of Hawaii, Kapiolani Medical Center for Women and Children's Health, Honolulu, Hawaii, USA.
  • Christopher T F Huang
    Advanced Reproductive Center of Hawaii, Kapiolani Medical Center for Women and Children's Health, Honolulu, Hawaii, USA.
  • Chelsea Yin
    Pacific In Vitro Fertilization Institute and John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
  • Yousuf Harun
    Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
  • Hyeong J Ahn
    Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii, USA.
  • Aaron Ohta
    Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, Hawaii, USA.

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