A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images.

Journal: Computer methods and programs in biomedicine
PMID: 33524814

Abstract

BACKGROUND AND OBJECTIVE: The morphology of the human metaphase II (MII) oocyte is an essential indicator of the embryo's potential for developing into a healthy baby in the Intra-Cytoplasmic Sperm Injection (ICSI) process. In this case, characteristics such as oocyte and ooplasm area, zona pellucida (ZP) thickness, and perivitelline space (PVS) width are also linked to the embryo's implantation potential. Moreover, oocyte segmentation methods may be of particular interest in those countries' restrictive IVF legislation.

Authors

  • Sedighe Firuzinia
    Department of Computer Engineering, University of Guilan, Rasht, Iran.
  • Seyed Mahmoodreza Afzali
    Department of Computer Engineering, University of Guilan, Rasht, Iran.
  • Fatemeh Ghasemian
    Department of Biology, University of Guilan, Rasht, Iran. Electronic address: ghasemian@guilan.ac.ir.
  • Seyed Abolghasem Mirroshandel
    Department of Computer Engineering, University of Guilan, Rasht, Iran.

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