Windows

AnyLabeling is Effortless AI-assisted data labeling tool with AI support from Segment Anything and YOLO models!

AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling

Installation

Standalone (executable)

The executable file links are provided in Assets section here

Install from source

git clone https://github.com/vietanhdev/anylabeling
cd anylabeling
pip install .

Install from PyPI

pip install anylabeling

With GPU support:

pip install anylabeling-gpu

NODeJ is an ImageJ plugin for 3D segmentation of nuclear objects.

"The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis . The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi-org.osaka-u.idm.oclc.org/10.15454/1HSOIE ."

An imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons.

BraiAn is an open-source suite of tools designed to simplify signal quantification, analysis and visualization of large datasets typically obtained in whole-brain imaging experiments, following registration to an atlas. 

The package consists of two separate modules.

1. BrainAnDetect: A QuPath extension for multi-channel cell segmentation across large and variable datasets. It leverages QuPath's built in algorithms for cell detection, and features additional options for refining signal quantification, including machine-learning-based object classification, region-specific cell segmentation, multiple marker co-expression analysis, and an interface for selective exclusion of damaged tissue portions.
2. BraiAnalyse: A modular Python library for the easy navigation, visualization, and analysis of whole-brain quantification outputs.

A generalist framework for multi-dimensional automatic spot detection and quantification.

SpotMAX is designed to accomplish two tasks:

1. Detecting and quantifying globular-like structures (a.k.a. "spots")
2. Segmenting and quantifying fluorescently labelled structures

It supports 2D, 3D, 4D, and 5D data, i.e., z-stacks, timelapse, and multiple fluorescence channels (and combinations thereof).