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Communication Dans Un Congrès Année : 2022

AAEGAN Optimization by Purposeful Noise Injection for the Generation of Bright-Field Brain Organoid Images

Résumé

Brain organoids are three-dimensional tissues generated in vitro from pluripotent stem cells and replicating the early development of Human brain. To implement, test and compare methods to follow their growth on microscopic images, a large dataset not always available is required with a trusted ground truth when developing automated Machine Learning solutions. Recently, optimized Generative Adversarial Networks prove to generate only a similar object content but not a background specific to the real acquisition modality. In this work, a small database of brain organoid bright field images, characterized by a shot noise background, is extended using the already validated AAEGAN architecture, and specific noise or a mixture noise injected in the generator. We hypothesize this noise injection could help to generate an homogeneous and similar bright-field background. To validate or invalidate our generated images we use metric calculation, and a dimensional reduction on features on original and generated images. Our result suggest that noise injection can modulate the generated image backgrounds in order to produce a more similar content as produced in the microscopic reality. A validation of these images by biological experts could augment the original dataset and allow their analysis by Deep-based solutions.
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Dates et versions

hal-03631030 , version 1 (05-04-2022)

Identifiants

  • HAL Id : hal-03631030 , version 1

Citer

Clara Brémond Martin, Camille Simon Chane, Cedric Clouchoux, Aymeric Histace. AAEGAN Optimization by Purposeful Noise Injection for the Generation of Bright-Field Brain Organoid Images. IPTA 2022, Apr 2022, Salzburg, Austria. ⟨hal-03631030⟩
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