Workflow

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. 

ImageJ macro script to streamline the original NMJ-morph methodology doi:10.1098/rsob.160240
Also requires Binary Connectivity https://blog.bham.ac.uk/intellimic/g-landini-software/ 

Scripts that allow to automatize  a multiplex analysis in qupath. The input is a multi channel image with one channel containing a staining of cell nuclei and the other channels containing markers for a specific molecule. The goal is to obtain a count of positive cells for the markers in the different channels and for the combinations of positive markers. The workflow also adds the total numbers of positive cells for each marker to the results table (cells positive for multiple markers are not counted positive for the individual markers in the original qupath result).

Spine Analyzer allows to semi-automatically segment dendritic spines in 3D+t images and to measure their volumes and the intensities of the signal within in different channels over time.

HistoMetrix is an advanced histology analysis software designed to simplify image processing and analysis for biologists and pathologists. Powered by the most advanced deep learning technology, HistoMetrix enables you to effortlessly uncover valuable insights and visualize results without the need for extensive technical expertise. Let’s explore the key features that make HistoMetrix the ultimate solution for histology analysis.

HistoMetrix leverages cutting-edge deep learning technology to streamline and simplify image processing for histology analysis. You can easily navigate through complex datasets, detect and analyze tissue structures, and extract meaningful information with just a few clicks. Research use only.

HistoMetrix combines advanced deep learning technology with cost-effectiveness, making it the ideal histology analysis software for biologists and pathologists. With HistoMetrix, effortlessly uncover valuable insights, visualize results, and simplify image processing, all while enjoying significant cost savings compared to other solutions. Benefit from affordable pricing plans, no expensive hardware requirements, and no need for costly training programs. HistoMetrix streamlines workflows, automates tasks, and provides efficient analysis tools, allowing you to save valuable time and resources. Experience the cost-effective solution for histology analysis and accelerate your research with HistoMetrix.