Synthesis of large scale 3D microscopic images of 3D cell cultures for training and benchmarking.

ABSTRACT: The analysis of 3D microscopic cell culture images plays a vital role in the development of new therapeutics. While 3D cell cultures offer a greater similarity to the human organism than adherent cell cultures, they introduce new challenges for automatic evaluation, like increased heterogeneity. Deep learning algorithms are able to outperform conventional analysis methods in such conditions but require a large amount of training data. Due to data size and complexity, the manual annotation of 3D images to generate large datasets is a nearly impossible task. We therefore propose a pipeline that combines conventional simulation methods with deep-learning-based optimization to generate large 3D synthetic images of 3D cell cultures where the labels are known by design. The hybrid procedure help

SUBMITTER: Bruch R 

PROVIDER: S-EPMC10065262 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature