# Using Deep Learning for Segmentation and Counting within Microscopy Data

arXiv: https://arxiv.org/abs/1802.10548 · HTML: https://mlsanity.com/paper/1802.10548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases](https://mlsanity.com/paper/2406.19556/) (2024)
- [Optimized Learning for X-Ray Image Classification for Multi-Class Disease Diagnoses with Accelerated Computing Strategies](https://mlsanity.com/paper/2407.01705/) (2024)

## Abstract (arXiv)

Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation. From basic biological questions to clinical trials, cell counts provide key quantitative feedback that drive research. Unfortunately, cell counting is most commonly a manual task and can be time-intensive. The task is made even more difficult due to overlapping cells, existence of multiple focal planes, and poor imaging quality, among other factors. Here, we describe a convolutional neural network approach, using a recently described feature pyramid network combined with a VGG-style neural network, for segmenting and subsequent counting of cells in a given microscopy image.