  Deep learning provides us with powerful methods to perform nucleus or cell
segmentation with unprecedented quality. However, these methods usually require
large training sets of manually annotated images, which are tedious and
expensive to generate. In this paper we propose to use In Silico Labeling (ISL)
as a pretraining scheme for segmentation tasks. The strategy is to acquire
label-free microscopy images (such as bright-field or phase contrast) along
fluorescently labeled images (such as DAPI or CellMask). We then train a model
to predict the fluorescently labeled images from the label-free microscopy
images. By comparing segmentation performance across several training set
sizes, we show that such a scheme can dramatically reduce the number of
required annotations.
