Cellcounter: a deep learning framework for high-fidelity spatial localization of neurons

TL;DR

Cellcounter is a deep learning framework that accurately localizes neurons in high-resolution images with minimal manual annotation, effectively handling variability and artifacts in neuroscientific imaging.

Contribution

It introduces a self-learning deep model trained on incompletely annotated data, reducing manual effort and improving neuron localization accuracy.

Findings

Outperforms existing methods in neuron localization accuracy.

Reduces false positives significantly.

Effective on diverse imaging protocols.

Abstract

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.