The advent of deep learning and open access to a substantial collection of imaging data provide a potential solution to computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current deep-learning implementations usually operate in a supervised manner, and the reliance on a laborious and error-prone data annotation procedure remains a barrier towards more general applicability. Here, we propose an unsupervised image transformation enlightened by cycle-consistent generative adversarial networks (cycleGANs) to facilitate the utilization of deep learning in optical microscopy. By incorporating the saliency constraint into cycleGAN, the unsupervised approach, dubbed as content-preserving cycleGAN (c2GAN), can learn the mapping between two image domains and avoid the misalignment of salient objects without paired training data. We demonstrate several image transformation tasks such as fluorescence image restoration, whole-slide histological coloration, and virtual fluorescent labeling. Quantitative evaluations prove that c2GAN achieves robust and high-fidelity image transformation across different imaging modalities and various data configurations. We anticipate that our framework will encourage a paradigm shift in training neural networks and democratize deep learning algorithms for optical imaging.

Unsupervised content-preserving image transformation for optical microscopy