Enhanced cell segmentation with limited annotated data using generative adversarial networks.

ABSTRACT: The application of deep learning is rapidly transforming the field of bioimage analysis. While deep learning has shown great promise in complex microscopy tasks such as single-cell segmentation, the development of generalizable foundation deep learning segmentation models is hampered by the scarcity of large and diverse annotated datasets of cell images for training purposes. Generative Adversarial Networks (GANs) can generate realistic images that can potentially be easily used to train deep learning models without the generation of large manually annotated microscopy images. Here, we propose a customized CycleGAN architecture to train an enhanced cell segmentation model with limited annotated cell images, effectively addressing the challenge of paucity of annotated data in microscopy ima

SUBMITTER: Zargari A 

PROVIDER: S-EPMC10402092 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature