  Best performing nuclear segmentation methods are based on deep learning
algorithms that require a large amount of annotated data. However, collecting
annotations for nuclear segmentation is a very labor-intensive and
time-consuming task. Thereby, providing a tool that can facilitate and speed up
this procedure is very demanding. Here we propose a simple yet efficient
framework based on convolutional neural networks, named NuClick, which can
precisely segment nuclei boundaries by accepting a single point position (or
click) inside each nucleus. Based on the clicked positions, inclusion and
exclusion maps are generated which comprise 2D Gaussian distributions centered
on those positions. These maps serve as guiding signals for the network as they
are concatenated to the input image. The inclusion map focuses on the desired
nucleus while the exclusion map indicates neighboring nuclei and improve the
results of segmentation in scenes with nuclei clutter. The NuClick not only
facilitates collecting more annotation from unseen data but also leads to
superior segmentation output for deep models. It is also worth mentioning that
an instance segmentation model trained on NuClick generated labels was able to
rank first in LYON19 challenge.
