multi-channel

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).

This workflow is the integration of YOLO (You Only Look Once) machine learning models, image pre-processing scripts and labeling tools within the Galaxy platform. Galaxy is an open, web-based platform used primarily for data analysis in computational biology, but it also has applications in image processing and other fields. 

How the Galaxy YOLO image segmentation tool works

The combination of Galaxy and YOLO allows researchers to perform object detection and image analysis without requiring extensive programming knowledge. Here's how it generally works: 

• Web-based interface: Galaxy provides a graphical, user-friendly interface to access powerful analysis tools. Users can simply upload their image data, select the YOLO tool, and run the analysis.
• YOLO model execution: The Galaxy tool executes a pre-trained YOLO model, often from the Ultralytics framework, on the input images. These models can perform tasks like object detection (drawing bounding boxes) or instance segmentation (creating pixel-level masks).
• Training and prediction: Some tools allow for both model training and prediction. Users can train a custom YOLO model on their own labeled datasets to detect specific objects of interest. For example, bioimage analysis may involve detecting cells or other structures.
• Other integrations: Other machine-learning tools can be integrated with YOLO in Galaxy. For instance, the AnyLabeling tool supports YOLO for semi-automated and active learning-based data annotation. 

Description from Github page:

A GUI-based Python framework for segmentation, tracking, cell cycle annotations and quantification of microscopy data.
Provides a GUI for neural network models including Segment Anything Model (SAM), YeaZ, cellpose, StarDist, YeastMate, omnipose, delta, DeepSea.

MetaXpress or in full name "MetaXpress® High-Content Image Acquisition and Analysis Software" is a commercially available closed source software for high-content analysis from Molecular Devices, LLC.. The program is a kind of visually guided workflow programming environment. There is a programming module called CME (custom module editor) which lets one setup integrated workflows for bioimage analysis with visual feedback. It is designed for high-throughput in connection with a included database which stores the experimental data. 

It has several toolboxes for semiautomated processing of various tasks:

3D Analysis (requires Custom Module Editor), Curve fitting, Transmitted light segmentation (requires Custom Module Editors), Angiogenesis tube formation, Cell cycle, Cell health, Cell scoring , Count nuclei, Granularity, Live/dead , Mitotic index, Micronuclei , Monopole detection, Multi-Wavelength cell scoring, Multi-wavelength translocation, Neurite outgrowth , Transfluor® Assay, Translocation* (includes Translocation-Enhanced*) , Transfluor HT Assay , Nuclear translocation HAT, Cell proliferation HT

After the workflow is setup it is possible to apply it automatically to a stack of stored images. The derived data from those analyses is stored in the metaxpress database and can be exported from there.

The use of each toolbox requires a separate license.

# Install the ultralytics package from PyPI
pip install ultralytics

You can also install ultralytics directly from the Ultralytics GitHub repository. This can be useful if you want the latest development version. Ensure you have the Git command-line tool installed, and then run:

# Install the ultralytics package from GitHub
pip install git+https://github.com/ultralytics/ultralytics.git@main