  Automated cellular instance segmentation is a process utilized for
accelerating biological research for the past two decades, and recent
advancements have produced higher quality results with less effort from the
biologist. Most current endeavors focus on completely cutting the researcher
out of the picture by generating highly generalized models. However, these
models invariably fail when faced with novel data, distributed differently than
the ones used for training. Rather than approaching the problem with methods
that presume the availability of large amounts of target data and computing
power for retraining, in this work we address the even greater challenge of
designing an approach that requires minimal amounts of new annotated data as
well as training time. We do so by designing specialized contrastive losses
that leverage the few annotated samples very efficiently. A large set of
results show that 3 to 5 annotations lead to models with accuracy that: 1)
significantly mitigate the covariate shift effects; 2) matches or surpasses
other adaptation methods; 3) even approaches methods that have been fully
retrained on the target distribution. The adaptation training is only a few
minutes, paving a path towards a balance between model performance, computing
requirements and expert-level annotation needs.
