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Human oocytes image classification method based on deep neural networks.

Anna Targosz1,2, Dariusz Myszor3, Grzegorz Mrugacz4

  • 1Department of Histology and Embryology, Faculty of Medical Sciences, Medical University of Silesia, 18 Medyków St, 40-752, Katowice, Poland. atargosz@klinikabocian.pl.

Biomedical Engineering Online
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Summary

This study introduces an automated method using deep neural networks (DNNs) for classifying human oocytes, improving in vitro fertilization success rates. The DNN model achieved high accuracy in identifying oocyte maturity stages from microscopic images.

Keywords:
Artificial intelligenceClassificationDeep neural networkHuman oocyteIVFMachine learning

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Area of Science:

  • Reproductive biology
  • Biomedical engineering
  • Artificial intelligence in medicine

Background:

  • In vitro fertilization (IVF) success hinges on selecting oocytes and embryos with high developmental potential.
  • Accurate classification of oocyte meiotic maturity is crucial for intracytoplasmic sperm injection (ICSI).
  • Traditional manual oocyte classification under a microscope is subjective and time-consuming.

Purpose of the Study:

  • To develop an automated system for classifying human oocytes based on microscopic images.
  • To improve the efficiency and accuracy of oocyte selection for assisted reproductive technologies.
  • To leverage deep neural network (DNN) algorithms for oocyte maturity assessment.

Main Methods:

  • Employed a two-stage deep neural network approach for automated oocyte classification.
  • Utilized DeepLabV3Plus for feature extraction from oocyte images.
  • Applied a SqueezeNet-inspired network, optimized by a genetic algorithm, for oocyte type classification (MI, MII, PI).

Main Results:

  • Achieved a classification accuracy of 0.964 on the validation set and 0.957 on the test set.
  • The genetic algorithm refined the network for improved generalization and reduced computational cost (FLOPs).
  • The developed pipeline enables automatic classification of human oocytes into MI, MII, and PI stages.

Conclusions:

  • A complete pipeline for automated human oocyte classification from microscopic images was successfully developed.
  • The automated system offers a promising tool for enhancing oocyte selection in IVF and ICSI procedures.
  • Publicly released code and trained neural networks facilitate further research and clinical application.