  Many neuroscientific applications require robust and accurate localization of
neurons. It is still an unsolved problem because of the enormous variation in
intensity, texture, spatial overlap, morphology and background artifacts. In
addition, curation of a large dataset containing complete manual annotation of
neurons from high-resolution images to train a classifier requires significant
time and effort. We present Cellcounter, a deep learning-based model trained on
images containing incompletely-annotated neurons with highly-varied morphology
and control images containing artifacts and background structures. Leveraging
the striking self-learning ability, Cellcounter gradually labels neurons,
obviating the need for time-intensive complete annotation. Cellcounter shows
its efficacy over the state of the arts in the accurate localization of neurons
while significantly reducing false-positive detection in several protocols.
