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.