Synthetic Generation of 3D Microscopy Images using Generative Adversarial Networks.

Fluorescence microscopy images of cell organelles enable the study of various complex biological processes. Recently, deep learning (DL) models are being used for the accurate automatic analysis of these images. DL models present state-of-the-art performance in many image analysis tasks such as object classification, segmentation and detection. However, to train a DL model a large manually annotated dataset is required. Manual annotation of 3D microscopy images is a time-consuming task and must be performed by specialists in the area. Thus, only a few images with annotations are typically available. Recent advances in generative adversarial networks (GANs) have allowed the translation of images with some conditions into realistic looking synthetic images. Therefore, in this work we explore approaches based on GANs to create synthetic 3D microscopy images. We compare four approaches that differ in the conditions of the input image. The quality of the generated images was assessed visually and using a quantitative objective GAN evaluation metric. The results showed that the GAN is able to generate synthetic images similar to the real ones. Hence, we have presented a method based on GANs to overcome the issue of small annotated datasets in the biomedical imaging field.