Scalable Instance Segmentation using PyTorch & PyTorch Lightning.
Count-Ception: Counting by Fully Convolutional Redundant Counting
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.
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.
The code of paper: Lite-UNet: A Lightweight and Efficient Network for Cell Localization
Cell image analysis pipeline for RPE cell identification, counting and maturity classification
Efficient point process inference for large scale object detection
Semi-automated script for detection and quantification of c-Fos cells in IHC stained confocal stack images
SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.
Medical Image processing and segmentation for the automatic detection and counting of blood platelets and WBCs.
MATLAB-based software for cell counting
braincellcount: count cells in mouse brains
A simple ImageJ Macro to count the cells of multiple images.
Browser-based Neubauer count and inoculum calculator for CHO cell culture passaging.
A Head-less Python Tool for Automated Cell Counting in Cropped Hemocytometer Frames
Region-based Fitting of Overlapping Ellipses (original implementation by C. Panagiotakis and A.A. Argyros, Image Vis Comput 2020)
3D single-cell cFOS counting and reconstruction in thick, optically cleared tissue z-stacks
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