Instance Segmentation with PyTorch & PyTorch Lightning.
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Updated
May 29, 2024 - Python
Instance Segmentation with PyTorch & PyTorch Lightning.
Count-Ception: Counting by Fully Convolutional Redundant Counting
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.
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
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.
Non invasive live cell cycle monitoring using a supervised deep neural autoencoder onquantitative phase images
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.
This repository is dedicated to the AIBI 2019/2020 project. This project's objective is to automate cell counting in microscopy images.
count lipid droplets in tetrahymena with Python
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
An immunohistochemistry cell-counting (quantifying) neural network (CSRNet PyTorch) that was trained on KRT14, KRT5, and Ki67 stains (and of course DAPI).
SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.
braincellcount: count cells in mouse brains
Plugins for ImageJ/FIJI
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