Recently added (10)#

BioImage.IO Chatbot, GloBIAS Seminar#

Caterina Fuster-Barcelo

Published 2024-10-02

Licensed CC-BY-4.0

The dynamic field of bioimage analysis continually seeks innovative tools to democratize access to analysis tools and its documentation. The BioImage.IO Chatbot, leveraging state-of-the-art AI technologies including Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), provides an interactive platform that significantly integrates the exploration and application of bioimage analysis tools and models. This seminar will introduce the BioImage.IO Chatbot’s capabilities, focusing on how it facilitates access to advanced analysis tools and documentation, allows for the execution of complex models, and enables users to create customized extensions adjusted to specific research needs. In a live demo, attendees will see how to interact with the chatbot and all its assistants and capabilities. Join us to explore how the BioImage.IO Chatbot ca transform your research by making sophisticated analysis more intuitive and accessible.

https://zenodo.org/records/13880367

https://doi.org/10.5281/zenodo.13880367


Developing a Training Strategy#

Robert Haase

Published 2024-11-08

Licensed CC-BY-4.0

When training people in topics such as programming, bio-image analysis or data science, it makes sense to define a training strategy with a wider perspective than just trainees needs. This slide deck gives insights into aspects to consider when defining a training strategy. It considers funder’s interests, financial aspects, metrics / goals, steps towards sustainability and opportunities for outreach and for founding future collaborations.

https://zenodo.org/records/14053758

https://doi.org/10.5281/zenodo.14053758


Gut Analysis Toolbox#

Luke Sorensen, Ayame Saito, Sabrina Poon, Noe Han, Myat, Ryan Hamnett, Peter Neckel, Adam Humenick, Keith Mutunduwe, Christie Glennan, Narges Mahdavian, JH Brookes, Simon, M McQuade, Rachel, PP Foong, Jaime, Estibaliz Gómez-de-Mariscal, Muñoz Barrutia, Arrate, Kaltschmidt, Julia A., King, Sebastian K., Robert Haase, Simona Carbone, A. Veldhuis, Nicholas, P. Poole, Daniel, Pradeep Rajasekhar

Published 2025-02-23

Licensed BSD-3-CLAUSE

Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v0.7…v1.0 Skip versions to 1.0 Fixed major bugs:

Use deepImageJ to run Stardist models, due to issue with tensorflow in Fiji Fixed ganglia model to be compatible with new versions of deepImageJ Updated all scripts to accommodate for new deepImageJ workflow Added macros to generate user dialog when running GAT for first time

https://zenodo.org/records/14913673

https://doi.org/10.5281/zenodo.14913673


Image Analysis using Galaxy#

Beatriz Serrano-Solano, Leonid Kostrykin, Anne Fouilloux, Riccardo Massei

Published 2025-02-28

Licensed CC-BY-4.0

GloBIAS seminar series   Part 3 in the topic:  Infrastructure for deploying image analysis workflows Image analysis using Galaxy

Beatrix Serrano-Solano, Euro-BioImaging ERIC Bio-Hub, EMBL Heidelberg, Germany & Anne Fouilloux , Simula Research Laboratory, Oslo, Norway & Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University, BioQuant, IPMB, Heidelberg, Germany & Ricardo Massei, Helmholtz Center for Environmental Research, UFZ, Leipzig, Germany Abstract: This webinar will introduce the Galaxy Image Analysis Community and highlight our mission to advance the development of FAIR and reproducible image analysis workflows. As part of our commitment to making image data analysis more accessible and collaborative, we will showcase how Galaxy can serve the imaging community. The session will explore Galaxy’s capabilities for integrating popular image analysis tools, interactive environments, and notebooks, making it a versatile platform for researchers across various scientific domains. We will also present how Galaxy facilitates the creation and sharing of reusable workflows, promoting open science and fostering collaboration. To give participants hands-on insight, we’ll provide a live demonstration on designing and running image analysis workflows within Galaxy. 

 

https://zenodo.org/records/14944040

https://doi.org/10.5281/zenodo.14944040


LauLauThom/MaskFromRois-Fiji: v1.0.1 - better handle “cancel”#

Laurent Thomas, Pierre Trehin

Published 2025-02-24

Licensed MIT

Also re-uploaded the compiled FilenameGetter.py$class to the update site, to fix LauLauThom/MaskFromRois-Fiji#7

https://zenodo.org/records/14917722

https://doi.org/10.5281/zenodo.14917722


Leica (.lif) file with errors in channel order when imported with Bio-formats#

Areli Rodriguez

Published 2025-02-26

The blue and red channels get swapped when imported with Bio-formats. Happens consistently with .lif imports in QuPath and ImageJ.

https://zenodo.org/records/14933318

https://doi.org/10.5281/zenodo.14933318


NFDI4Bioimage Calendar 2025 March; original image#

Sonja Schimmler, Reinhard Altenhöner, Lars Bernard, Juliane Fluck, Axel Klinger, Sören Lorenz, Brigitte Mathiak, Bernhard Miller, Raphael Ritz, Thomas Schörner-Sadenius, Alexander Sczyrba, Regine Stein

Published 2025-02-27

Licensed CC-BY-4.0

Raw microscopy image from the NFDI4Bioimage calendar March 2025. The image shows 125x magnified microscopic details of a biofilm formed by Pseudomonas fluorescence on the surface of a liquid culture medium. The culture was inoculated with a cellulose-overexpressing and surface-colonizing mScarlet-tagged wild type and a GFP-tagged mutant that is unable to colonize the surface. The biofilm can collapse over time due to its own mass, so that new strategies have to be developed and thus a life cycle emerges. Image Metadata (using REMBI template):

Study  

Study description Biofilm formation

Study Component  

Imaging method Stereo microscopy

Biosample  

Biological entity Bacteria

Organism Pseudomonas fluorescence

Specimen  

Signal/contrast mechanism Relief, fluorescence

Channel 1 - content Relief, grey

Channel 1 - biological entity Details of the biofilm in transmitted light

Channel 2 - content mScarlet, red

Channel 2 - biological entity WT over-expressing cellulose and colonizing the surface

Channel 3 - content GFP, green

Channel 3 - biological entity ∆wss mutant unable to colonize the surface

Image Acquisition  

Microscope model Zeiss Axio Zoom V16

Image Data  

Magnification 125x

Objective PlanNeoFluar Z 1.0x

Dimension extents x: 2752, y: 2208

Pixel size description 0.91 µm x 0.91 µm

Image area 2500µm x 2500µm

 

https://zenodo.org/records/14937632

https://doi.org/10.5281/zenodo.14937632


Reconstructed images of a 2DSIM multiposition acquisition.#

Louis Romette

Published 2025-02-19

Licensed CC-BY-4.0

Acquired with an Nikon SIM, in 2D-SIM mode at 488nm of excitation with 30% laser power and 200ms of exposition.  Fluorescence is a knocked-in mStayGold-β2Spectrin. Cells are rat hippocampal neurons à DIV 3. The file is a reconstructed multiposition acquisition (10 positions). Uploaded to show a probable issue with Bio-Formats in Fiji, where SIM reconstrcuted multipositions files open like static noise. 

https://zenodo.org/records/14893791

https://doi.org/10.5281/zenodo.14893791


Training Computational Skills in the Age of AI#

Robert Haase

Published 2024-11-06

Licensed CC-BY-4.0

Artificial intelligence (AI) and large language models (LLMs) are changing the way we use computers in science. This slide deck introduces ways for using AI and LLMs for making training materials and for exchanging knowledge about how to use AI in joint discussions between humans and LLM-based AI-systems.

https://zenodo.org/records/14043615

https://doi.org/10.5281/zenodo.14043615


napari-scipy2025-workshop#

Draga Doncila Pop

Published 2025-02-28T23:41:10+00:00

Licensed BSD-3-CLAUSE

This is a three-part workshop guiding you through using napari to view images, a brief bioimaging analysis application, and extending napari’s functionality with your own custom workflows.

Tags: Python, Napari

Content type: Github Repository

DragaDoncila/napari-scipy2025-workshop