Task-Assisted GAN for Resolution Enhancement and Modality Translation in Fluorescence Microscopy

We introduce a deep learning model for resolution enhancement and prediction of super-resolved biological structures, which is based on a Generative Adversarial Network (GAN) assisted by a complementary segmentation task. It is applied to predict biological nanostructures from diffraction-limited images and to guide microscopists for quantitative fixed- and live-cell STimulated Emission Depletion (STED) microscopy. More specifically, we show that the use of a complementary segmentation task improves the accuracy of the predicted nanostructures over state-of-the art resolution enhancement generative approaches, allowing quantitative analysis of the sub-diffraction structures in the resulting generated images.