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HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks.

Darius Dirvanauskas1, Rytis Maskeliūnas1, Vidas Raudonis2

  • 1Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania.

Sensors (Basel, Switzerland)
|August 21, 2019
PubMed
Summary

This study introduces a novel method for generating realistic synthetic human embryo cell images using a deep neural network and generative adversarial network (GAN). These synthetic images aid in training AI models for embryo analysis, especially where real data is scarce.

Keywords:
deep learninggenerative adversarial networkneural networksynthetic images

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Area of Science:

  • Developmental Biology
  • Biomedical Imaging
  • Artificial Intelligence

Background:

  • Limited availability of real-world human embryo cell images hinders AI model development.
  • Accurate classification and analysis of embryo development are crucial for reproductive medicine.

Purpose of the Study:

  • To develop a method for generating realistic synthetic human embryo cell images.
  • To create new synthetic image datasets for research and AI training.
  • To ensure synthetic images possess attributes of real cell images for maximum utility.

Main Methods:

  • Utilized generative adversarial networks (GANs) trained on human embryo images.
  • Generated synthetic images for one-, two-, and four-cell developmental stages.
  • Employed deep neural networks (DNNs) for image generation and analysis.

Main Results:

  • Achieved a misclassification rate of 12.3% for generated images.
  • Expert evaluation demonstrated high true recognition rates: 96.2% (one-cell), 86.8% (two-cell), and 80.0% (four-cell).
  • Texture analysis (Haralick features) revealed no significant statistical differences between real and synthetic images (p < 0.01), except for specific variance and average features.

Conclusions:

  • The proposed GAN-based method effectively generates realistic synthetic human embryo cell images.
  • These synthetic images can augment datasets, facilitating the development and evaluation of embryo image processing algorithms.
  • The approach addresses data scarcity issues in human embryo research and AI development.