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Brain organoid data synthesis and evaluation.

Clara Brémond-Martin1,2, Camille Simon-Chane1, Cédric Clouchoux2

  • 1ETIS Laboratory UMR 8051 (CY Cergy Paris Université, ENSEA, CNRS), Cergy, France.

Frontiers in Neuroscience
|August 31, 2023
PubMed
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Generative Adversarial Networks (GANs) create realistic synthetic biomedical images from small datasets. Experts could not distinguish GAN-generated images from real ones, improving downstream AI tasks.

Area of Science:

  • Biomedical imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Small datasets are a challenge for deep learning in biomedicine.
  • Generative Adversarial Networks (GANs) can augment limited image datasets.
  • Validating synthetic images and expert evaluation are time-consuming.

Purpose of the Study:

  • To augment a small brain organoid dataset using GANs.
  • To compare synthetic and real images using metrics and expert evaluation.
  • To assess the utility of validated synthetic images in a segmentation task.

Main Methods:

  • Augmented a 40-image brain organoid dataset with GANs.
  • Performed psychovisual evaluation with eight biological experts on 280 images.
  • Calculated error rates, hesitation times, and compared with similarity metrics.
Keywords:
AAEbrain organoidmetricpsychovisualvalidation

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  • Tested psychovalidated images in a segmentation task.

    • Main Results:

    • Generated images were indistinguishable from real images by experts.
    • Perceptual and Wasserstein loss optimizations produced the most realistic images.
    • Some metric combinations showed potential to replace psychovisual evaluation.
    • Segmentation tasks using validated synthetic images achieved higher accuracy.

    Conclusions:

    • GANs effectively generate high-quality synthetic biomedical images.
    • Expert validation is crucial but time-consuming; metrics can offer complementary insights.
    • Psychovalidated synthetic data improves performance in medical image analysis tasks.