Paper Info
Title
AAEGAN Optimization by Purposeful Noise Injection for the Generation of Bright-Field Brain Organoid Images
Abstract
Brain organoids are three-dimensional tissues gener-ated 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.
Year
DOI
Venue
2022
10.1109/IPTA54936.2022.9784149
2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
Keywords
DocType
ISSN
AAEGAN,noise injection,image generation,brain organoids,background modulation
Conference
2154-5111
ISBN
Citations 
PageRank 
978-1-6654-6965-4
0
0.34
References 
Authors
2
4
Name
Order
Citations
PageRank
Clara Bremond Martin100.34
Camille Simon Chane200.34
Cedric Clouchoux300.34
Aymeric Histace400.34