There are 2 repositories under cell-counting topic.
Instance Segmentation with PyTorch & PyTorch Lightning.
Count-Ception: Counting by Fully Convolutional Redundant Counting
This program is implemented to count the number of cells in the image. The cells are also labeled and the perimeter and area are calculated for each cell.
Cell localization and counting: 1) Exponential Distance Transform Maps for Cell Localization; 2) Multi-scale Hypergraph-based Feature Alignment Network for Cell Localization; 3) Lite-UNet: A lightweight and efficient network for cell localization
Analysis and characterisation of cells within the gut wall using deep learning models. The current focus is on studying enteric neurons and enteric glia.
Efficient point process inference for large scale object detection
The code of paper: Lite-UNet: A Lightweight and Efficient Network for Cell Localization
Semi-automated script for detection and quantification of c-Fos cells in IHC stained confocal stack images
Medical Image processing and segmentation for the automatic detection and counting of blood platelets and WBCs.
A demonstration script for analyzing cell density in whole slide images (WSIs). This repository accompanies the article published on daangeijs.nl. The demo showcases how to compute cell density in detected tumor regions using WholeSlideData and GeoPandas.
Non invasive live cell cycle monitoring using a supervised deep neural autoencoder onquantitative phase images
An immunohistochemistry cell-counting (quantifying) neural network (CSRNet PyTorch) that was trained on KRT14, KRT5, and Ki67 stains (and of course DAPI).
Plugins for ImageJ/FIJI
A short workshop for Matlab
A simple ImageJ Macro to count the cells of multiple images.