Fig. 6: Image-to-image translation networks (fnet, pix2pix and CycleGAN).

Example of data generated using the ZeroCostDL4Mic fnet, pix2pix and CycleGAN notebooks. a Scheme illustrating the data required to train paired image-to-image translation networks (pix2pix and fnet). b Fnet was trained to predict the location of mitochondria (Tom20 staining, Training Target) from bright-field images (Training Source). Both the fnet prediction and the ground-truth images are displayed. The quality control metrics values computed directly in the fnet notebook are as follows: mSSIM (mean structural similarity index): 0.79, PSNR (peak signal-to-noise ratio): 23.1 and NRMSE (normalised root-mean-squared error): 0.17. c pix2pix was trained to predict nuclear stainings (SiR-DNA, Training Target) from actin stainings (lifeact-RFP, Training Source) in migrating DCIS.COM cells. A pix2pix prediction, the corresponding ground-truth images are displayed. The quality control metrics values computed directly in the pix2pix notebook are as follows: mSSIM: 0.74, PSNR: 20.4 and NRMSE: 0.16. d Scheme illustrating the data requirement to train unpaired image-to-image translation networks (CycleGAN). Importantly, these networks do not need to have access to a paired training dataset. e, f CycleGAN was trained to predict what images of microtubules acquired with an SDC (spinning-disk confocal) would look like when processed with SRRF (super-resolution radial fluctuations) (e) (quality control metrics values are as follows: mSSIM: 0.74, PSNR: 24.8 and NRMSE: 0.19) or imaged with a SIM (structured illumination microscopy) microscope (f). A CycleGAN model was also trained to transform SRRF images into SIM images (g). For the SDC to SRRF translation, the CycleGAN prediction and ground-truth SRRF images are displayed as well as the QC metrics values computed directly in the pix2pix notebook are displayed. For all panels, yellow squares highlight a region of interest that is magnified.