Using Deep Learning for Segmentation and Counting within Microscopy Data

TL;DR

This paper presents a deep learning approach using a feature pyramid network combined with a VGG-style neural network to automate cell segmentation and counting in microscopy images, addressing challenges like overlapping cells and poor image quality.

Contribution

It introduces a novel convolutional neural network architecture specifically designed for accurate segmentation and counting of cells in microscopy data.

Findings

Effective segmentation of overlapping cells

Improved counting accuracy in challenging images

Potential for automation in biological research

Abstract

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.