A short workshop for Matlab

Cell image analysis pipeline for RPE cell identification, counting and maturity classification

Efficient point process inference for large scale object detection

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.

count lipid droplets in tetrahymena thermophila with Python

This repository is dedicated to the AIBI 2019/2020 project. This project's objective is to automate cell counting in microscopy images.

Non invasive live cell cycle monitoring using a supervised deep neural autoencoder onquantitative phase images

Medical Image processing and segmentation for the automatic detection and counting of blood platelets and WBCs.

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

Plugins for ImageJ/FIJI

The code of paper: Lite-UNet: A Lightweight and Efficient Network for Cell Localization

Region-based Fitting of Overlapping Ellipses (original implementation by C. Panagiotakis and A.A. Argyros, Image Vis Comput 2020)

braincellcount: count cells in mouse brains

Automated cell counting system for confocal microscope images using image processing techniques. Analyzes DAPI-stained neuronal samples, extracts cell features, and exports data for biological research.

SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.

Website for counting cells on a image

A simple ImageJ Macro to count the cells of multiple images.

Semi-automated script for detection and quantification of c-Fos cells in IHC stained confocal stack images

Counting number of cell in a picture of cells

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.