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 …