Highlights d Robust method automatically adapting to various unseen experimental scenarios d Deep learning solution for accurate nucleus segmentation without user interaction d Accelerates, improves quality, and reduces complexity of bioimage analysis tasks

Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is to adapt our model to unseen and unlabeled data using image style transfer to generate augmented training samples. This allows the model to recognize nuclei in new and different experiments without requiring expert annotations.Identifying nuclei is the starting point for many microscopy-based cellular analyses. Accurate localization of the nucleus is the basis of a variety of quantitative measurements, but is also a first step for identifying individual cell borders, which enables a multitude of further analyses. Until recently, the dominant approaches for this task have been based on classic image processing algorithms (e.g. CellProfiler 1 ) which were sometimes guided by shape and spatial priors 2 . A drawback of these methods is the need for expert knowledge to properly adjust the parameters, which typically must be re-tuned when experimental conditions change.Recently, deep learning has revolutionized an assortment of tasks in image analysis, from image classification 3 to face recognition 4 , and scene segmentation 5 . It is also responsible for breakthroughs in diagnosing retinal images 6 , classifying skin lesions with superhuman performance 7 , as well as incredible advances in 3D fluorescence image analysis 8 . However, aside from initial works from Caicedo et al. 9 and Van Valen et al. 10 , deep learning has yet to significantly advance nucleus segmentation performance.