Single-cell segmentation is typically a crucial task of image-based cellular analysis.
We present nucleAIzer, a deep-learning approach aiming toward a truly general method
for localizing 2D cell 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 that during training nucleAIzer automatically
adapts its nucleus-style model to unseen and unlabeled data using image style transfer
to automatically generate augmented training samples. This allows the model to recognize
nuclei in new and different experiments efficiently without requiring expert annotations,
making deep learning for nucleus segmentation fairly simple and labor free for most
biological light microscopy experiments. It can also be used online, integrated into
CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's
transparent peer review process is included in the Supplemental Information.
