Imaging

Image analysis using Galaxy

Requirements

Before diving into this topic, we recommend you to have a look at:

Material

You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides () and tutorial () buttons below.

Introduction

Start here if you are new to Image Analysis in Galaxy

Lesson Slides Hands-on Recordings Input dataset Workflows
FAIR Bioimage Metadata
Data management Metadata Bioimaging
REMBI - Recommended Metadata for Biological Images – metadata guidelines for bioimaging data
Data management Metadata Bioimaging
Introduction to Image Analysis using Galaxy
Image segmentation Image thresholding Conversion Object counting Overlay Fluorescence microscopy
Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy
Data management Data handling Data sharing ROI selection
Where to start with bioimage analysis in Galaxy
Microscopy Bioimaging Histology

Analyses

These tutorials will help you analyse your image data

Lesson Slides Hands-on Recordings Input dataset Workflows
Analyse HeLa fluorescence siRNA screen
Image segmentation Object feature extraction Image thresholding High-throughput screening Overlay Fluorescence microscopy
End-to-End Tissue Microarray Image Analysis with Galaxy-ME
Image segmentation Tissue imaging
Nucleoli segmentation and feature extraction using CellProfiler
Image segmentation Object feature extraction Multi-channel image
Object tracking using CellProfiler
Isolated object tracking Multi-channel image Earth observation
Quantification of electrophoresis gel bands using QuPath and Galaxy imaging tools
ROI selection Conversion Object feature extraction Multi-channel image
Quantification of single-molecule RNA fluorescence in situ hybridization (smFISH) in yeast cell lines
IDR dataset Spot detection Rasterisation Overlay
Quantitative Analysis of Histological Staining Using Color Deconvolution
Microscopy Bioimaging Histology pathology
Segmentation of Anatomical Structures in Medical 3-D Images
Image segmentation Conversion Overlay Object feature extraction Medical imaging Computed tomography 3D image Volume rendering
Tracking of mitochondria and capturing mitoflashes
Isolated object tracking Temporal analysis Time-lapse data
Use Pycytominer in Galaxy for processing high dimensional image-based readouts
Object feature extraction high-throughput data cleaning cellprofiler deepprofiler
Using BioImage.IO models for image analysis in Galaxy
Image segmentation Image annotation Deep learning
Voronoi segmentation
Tessellation Image segmentation Object counting Overlay Multi-channel image

Advanced topics

Tutorials covering advanced topics

Lesson Slides Hands-on Recordings Input dataset Workflows
Content Tracking and Verification in Galaxy Workflows with ISCC-SUM
Data management Data handling Data sharing Validation
Execute a BiaPy workflow in Galaxy
Image segmentation Image annotation Deep learning Conversion Overlay 3D image Volume rendering
Parameter tuning and optimization - Evaluating nuclei segmentation with Galaxy
Image segmentation Parameter optimisation Validation
Training Custom YOLO Models for Segmentation of Bioimages
Image segmentation Image annotation Deep learning

Other

Assorted Tutorials

Lesson Slides Hands-on Recordings Input dataset Workflows
AI pipeline for annotating marine species (Project Moorev - Marine)
Marine ecosystems biodiversity AI segmentation YOLO deep learning object detection

Frequently Asked Questions

Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.

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Community Resources

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Editorial Board

This material is reviewed by our Editorial Board:

orcid logoLeonid Kostrykin avatar Leonid Kostrykinorcid logoBeatriz Serrano-Solano avatar Beatriz Serrano-Solanoorcid logoDiana Chiang Jurado avatar Diana Chiang Jurado

Are you on the editorial board or want to help out with this topic? View the topic maintainer page for ways to improve this topic!

Contributors

This material was contributed to by:

Erik Serrano avatar Erik Serranoorcid logoAllison Creason avatar Allison CreasonDaniel Franco Barranco avatar Daniel Franco Barrancoorcid logoBeatriz Serrano-Solano avatar Beatriz Serrano-Solanoorcid logoYvan Le Bras avatar Yvan Le BrasEnis Afgan avatar Enis AfganLaura Cooper avatar Laura Cooperorcid logoBjörn Grüning avatar Björn Grüningorcid logoReyhaneh Tavakoli-Koopaei avatar Reyhaneh Tavakoli-Koopaeiorcid logoAnne Fouilloux avatar Anne Fouillouxorcid logoMartin Etzrodt avatar Martin Etzrodtorcid logoRiccardo Massei avatar Riccardo Masseiorcid logoAnup Kumar avatar Anup Kumarorcid logoDiana Chiang Jurado avatar Diana Chiang Juradoorcid logoMarius van den Beek avatar Marius van den Beekorcid logoKhaled Jum'ah avatar Khaled Jum'ahorcid logoCristóbal Gallardo avatar Cristóbal GallardoJean-Karim Hériché avatar Jean-Karim Hérichéorcid logoPavankumar Videm avatar Pavankumar Videmorcid logoMartin Čech avatar Martin ČechArthur Barreau avatar Arthur Barreauorcid logoKatarzyna Kamieniecka avatar Katarzyna Kamienieckaorcid logoHelena Rasche avatar Helena RascheMaria Arrate Munoz Barrutia avatar Maria Arrate Munoz BarrutiaKevin Payet avatar Kevin Payetorcid logoPaul De Geest avatar Paul De Geestorcid logoSaskia Hiltemann avatar Saskia HiltemannDave Bunten avatar Dave Buntenorcid logoArmin Dadras avatar Armin Dadrasorcid logoJulia Jakiela avatar Julia JakielaYi Sun avatar Yi Sunorcid logoMaarten Paul avatar Maarten Paulorcid logoDaniela Schneider avatar Daniela Schneiderorcid logoCameron Watson avatar Cameron Watsonorcid logoNadine Le Bris avatar Nadine Le Brisorcid logoMarie Josse avatar Marie Josseorcid logoKrzysztof Poterlowicz avatar Krzysztof Poterlowiczorcid logoThomas Wollmann avatar Thomas Wollmannorcid logoEven Moa Myklebust avatar Even Moa Myklebustorcid logoLeonid Kostrykin avatar Leonid KostrykinArif ul Maula Khan avatar Arif ul Maula Khan

Funding

These individuals or organisations provided funding support for the development of this resource

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DASH UK
This Fellowship was funded through the ELIXIR-UK DaSH project as part of the UKRI Innovation Scholars: Data Science Training in Health and Bioscience call (DaSH). (MR/V038966/1). The project aims to embed Research Data Management (RDM) know-how into UK universities and institutes by producing and delivering training in FAIR data stewardship using ELIXIR-UK knowledge and resources.
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OSCARS
The OSCARS project has received funding from the European Commission’s Horizon Europe Research and Innovation programme under grant agreement No. 101129751
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DFG
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MNHN
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NFDI4Bioimage
NFDI4BIOIMAGE is a nationwide community of researchers, imaging specialists and data experts who work with biological image data. We support anyone who generates, analyses or manages microscopy and bioimaging data. Our aim is to make imaging data easier to find, access, reuse and share by providing practical tools, clear guidance and hands on training. We collaborate closely with laboratories and research communities across Germany to understand their needs and develop solutions that help with real everyday challenges. Whether you are building new workflows, exploring advanced analysis methods or improving your data management, NFDI4BIOIMAGE offers support, expertise and a welcoming network.
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PNDB
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de.NBI
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SU
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ISYEB
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MOOREV
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ELIXIR Europe
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UFR
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OSCARS-FIESTA

References