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

arXiv: https://arxiv.org/abs/2103.10462 · HTML: https://mlsanity.com/paper/2103.10462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Class-Agnostic Counting](https://mlsanity.com/paper/1811.00472/) (2018)
- [MatConvNet - Convolutional Neural Networks for MATLAB](https://mlsanity.com/paper/1412.4564/) (2014)
- [Count-ception: Counting by Fully Convolutional Redundant Counting](https://mlsanity.com/paper/1703.08710/) (2017)
- [Enhanced Center Coding for Cell Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1904.08864/) (2019)
- [ElasticPath2Path: Automated morphological classification of neurons by elastic path matching](https://mlsanity.com/paper/1802.06913/) (2018)
- [NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity](https://mlsanity.com/paper/1903.02147/) (2019)

## Abstract (arXiv)

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.