Scalable Instance Segmentation using PyTorch & PyTorch Lightning.
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
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.
braincellcount: count cells in mouse brains
SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.
Cell image analysis pipeline for RPE cell identification, counting and maturity classification
Non invasive live cell cycle monitoring using a supervised deep neural autoencoder onquantitative phase images
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.
count lipid droplets in tetrahymena thermophila with Python
Plugins for ImageJ/FIJI
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