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
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.
Analysis and characterisation of cells within the gut wall using deep learning models. The current focus is on studying enteric neurons and enteric glia.
The code of paper: Lite-UNet: A Lightweight and Efficient Network for Cell Localization
Efficient point process inference for large scale object detection
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.
Semi-automated script for detection and quantification of c-Fos cells in IHC stained confocal stack images
Cell image analysis pipeline for RPE cell identification, counting and maturity classification
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
Region-based Fitting of Overlapping Ellipses (original implementation by C. Panagiotakis and A.A. Argyros, Image Vis Comput 2020)
A short workshop for Matlab
This repository is dedicated to the AIBI 2019/2020 project. This project's objective is to automate cell counting in microscopy images.
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