Computer vision for automatic identification of blastocyst structures and blastocyst formation time in In-Vitro Fertilization.

Journal: Computers in biology and medicine
Published Date: Jul 4, 2025

Abstract

Embryo selection is an indispensable step to ensure the success of In-Vitro Fertilization; however, this decision is a time-consuming, laborious, and highly subjective task for embryologists. In the best scenarios, when implanting an embryo of the best quality, the probability of pregnancy rate is just 34.1%. Automatic segmentation provides detailed, quantitative, and objective information about the embryo, reducing the workload of embryologists and enhancing the success rate of embryo implantation. As such, it represents a valuable resource in the field of assisted reproduction. Towards that aim, we present different computer vision methods that are able to automatically segment the different structures of the blastocyst - the first morphologically differentiated state of the embryo - with a Dice Score up to 0.89. Furthermore, our methods can identify the precise moment of the expanded blastocyst formation with a mean error of less than 4 h. We openly release the code so that anyone can use it and replicate the results. As a summary, this work aims to make the analysis of blastocysts more reliable and comparable, thereby advancing our understanding of embryo implantation.

Authors

  • María Villota
    Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50018, Aragón, Spain; Institute for Health Research Aragón (IIS Aragón), 50009, Aragón, Spain. Electronic address: mvillota@unizar.es.
  • Jacobo Ayensa-Jiménez
    Aragon Institute of Engineering Research (I3A), University of Zaragoza, Mariano Esquillor S/N, Zaragoza, Spain; Mechanical Engineering Department, University of Zaragoza, María de Luna S/N, Zaragoza, Spain; Aragon Institute of Health Research (IIS Aragón), University of Zaragoza, San Juan Bosco 13, Zaragoza, Spain. Electronic address: jacoboaj@unizar.es.
  • Clara Malo
    Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50018, Aragón, Spain; Institute for Health Research Aragón (IIS Aragón), 50009, Aragón, Spain.
  • Antonio Urries
    Institute of Assisted Human Reproduction, QuirónSalud Hospital Zaragoza, 50012, Aragón, Spain.
  • Manuel Doblaré
    Aragon Institute of Engineering Research (I3A), University of Zaragoza, Mariano Esquillor S/N, Zaragoza, Spain; Aragon Institute of Health Research (IIS Aragón), University of Zaragoza, San Juan Bosco 13, Zaragoza, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Monforte de Lemos 3-5, Pabellón 11. Planta 0, Madrid, Spain. Electronic address: mdoblare@unizar.es.
  • Jónathan Heras
    Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain.

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