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Motivation 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. Results This article presents a bioimage synthesis and assessment workflow with application to augment bacterial biofilm images. 3D cyclic generative adversarial networks (GAN) with unbalanced cycle consistency loss functions are exploited in order to synthesize 3D biofilm images from binary cell labels. Then, a stochastic synthetic dataset quality assessment (SSQA) measure that compares statistical appearance similarity between random patches from random images in two datasets is proposed. Both SSQA scores and other existing image quality measures indicate that the proposed 3D Cyclic GAN, along with the unbalanced loss function, provides a reliably realistic (as measured by mean opinion score) 3D synthetic biofilm image. In 3D cell segmentation experiments, a GAN-augmented training model also presents more realistic signal-to-background intensity ratio and improved cell counting accuracy. Availability and implementation https://github.com/jwang-c/DeepBiofilm. Supplementary information Supplementary data are available at Bioinformatics online. |
