3D GAN image synthesis and dataset quality assessment for bacterial biofilm

Jie Wang, Nazia Tabassum, Tanjin Taher Toma, Yibo Wang, Andreas Gahlmann, Scott T. Acton

Abstract: Data-driven deep learning techniques usually require a large quantity of labeled training data to achieve reliable solutions in bioimage analysis. However, noisy image conditions and high cell density in bacterial biofilm images make 3D cell annotations difficult to obtain. Alternatively, data augmentation via synthetic data generation is attempted, but current methods fail to produce realistic images.