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AI4Life & Leica deepen collaboration: 22 New BioImage.IO models now available in AIvia 15

AI4Life & Leica deepen collaboration: 22 New BioImage.IO models now available in Aivia 15

Leica Microsystems has rolled out Aivia 15, the newest version of its AI-driven image analysis software. Among its enhancements are 22 additional pre-converted BioImage.IO models

The release of Aivia 15 with its 22 new BioImage.IO models represents an important step in AI4Life’s mission to make AI-driven tools more accessible to life science researchers. With an improved interface, faster performance, and direct integration of community-curated models, Aivia 15 makes it easier for users to apply AI in their everyday workflows.

As part of the ongoing AI4Life–Leica collaboration, Aivia 15 now offers drag-and-drop support for 22 additional BioImage.IO models, bringing the total to 26 pre-converted models ready to use in Aivia’s recipe console.

This builds on the first phase of the collaboration launched in July 2023, when AI4Life and Leica began working together to make BioImage Model Zoo models interoperable with Aivia by converting them into a format directly usable within the software. You can read more about the initial collaboration here
https://ai4life.eurobioimaging.eu/ai4life-leica-collaboration/

By integrating these new models, AI4Life connects community-driven developments with industry users, broadening the choice of available models and supporting more reproducible research.

 

More information:

https://www.aivia-software.com/post/pancreatic-phase-contrast-cell-segmentation-bioimage-io

https://www.leica-microsystems.com/company/news/news-details/latest-ai-software-simplifies-image-analysis-and-speeds-up-insights-for-scientists/

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AI4Life & GloBIAS webinar series: 4 webinars covering the whole AI4Life ecosystem

AI4Life & GloBIAS webinar series: 4 webinars covering the whole AI4Life ecosystem

From February to August 2025, AI4Life joined forces with GloBIAS to host a 4-part webinar series, exploring how AI is transforming bioimage analysis — from accessing pre-trained models to building reproducible infrastructures and running open calls for the life sciences community.

The series ran every second month, on the second Thursday at 4 pm CE(S)T, and brought together experts from the AI4Life consortium to share practical tools and lessons learned. All sessions were recorded and are freely available on the AI4Life YouTube channel.

We were thrilled to welcome more than 200 registered participants across the series, with an average of 30–40 attendees per session, tuning in from across the globe.

Webinar 1 — AI4Life: What can it do for you and your images?

Speakers: Anna Kreshuk (EMBL), Wei Ouyang (SciLifeLab)
Chair: Beatriz Serrano-Solano (Euro-BioImaging)

This kick-off session introduced AI4Life and the BioImage Model Zoo. Anna and Wei walked us through “killer features” the unified model metadata standard, and how community tools like ilastik, Icy, and BiaPy can all run these models.

Webinar 2 — Towards FAIR and high-quality AI-ready data for bioimage analysis

Speakers: Teresa Zulueta-Coarasa (EMBL-EBI), Estibaliz Gómez de Mariscal (Human Technopole)
Chair: Ana Stojiljkovic (University of Bern)

All about annotated image datasets FAIR and ready for AI. Teresa presented the BioImage Archive’s approach to curating and annotating datasets, while Estibaliz shared insights on ensuring data quality and robust evaluation in the deep learning era.

Webinar 3 — Building scalable and reproducible AI infrastructure for Bioimaging

Speakers: Wei Ouyang (SciLifeLab), Iván Hidalgo-Cenalmor (BSC)
Chair: Marco dalla Vecchia (CNRS)

This session featured two complementary platforms:
– BioEngine: deploy and run BioImage Model Zoo models locally or institution-wide, with minimal hassle.
– DL4MicEverywhere: containerised workflows for reproducible, portable deep learning across Google Colab, desktops, and HPC clusters.

Webinar 4 — Lessons learned from 3 years of open calls and challenges

Speakers: Vera Galinova (HT), Mehdi Seifi (HT), Caterina Fuster-Barceló (UC3M)
Chair: Florian Jug (HT)

A behind-the-scenes look at AI4Life’s open calls and challenges: what worked, what we learned, and how our experiences can help image analysis facilities worldwide.

Although the AI4Life project is about to conclude, the recordings remain a rich resource for anyone interested in AI for microscopy and bioimaging.

 

Watch more content on the AI4Life YouTube channel: https://www.youtube.com/@ai4life 

 

 

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The 3rd AI4Life Challenge now live too

The 3rd AI4Life now live too

We’re excited to announce the AI4Life Calcium Imaging Denoising Challenge 2025, focused on unsupervised denoising of calcium imaging data, a critical step for improving signal extraction in functional microscopy.

Hosted on the Grand Challenge platform, we invite researchers, developers, and imaging enthusiasts to design robust, generalizable algorithms that preserve both the spatial structure and temporal dynamics of neuronal activity in calcium imaging.

Participants will:

  •  Access synthetic calcium imaging datasets with controlled noise levels.
  • Develop and submit unsupervised or self-supervised denoising models.
  • Be evaluated on spatial and temporal reconstruction quality using hidden ground truth data.
  • Address realistic generalization tasks across unseen content and varying noise settings.

Whether you’re exploring new ideas or validating established models, this challenge is a unique opportunity to contribute to core problems in bioimage analysis—and help advance reproducible, FAIR, and translational solutions for neuroscience and cell biology.

The challenge will run until August 11th.

Get started now: https://ai4life-cidc25.grand-challenge.org/

Whether you’re a seasoned researcher or new to the field, this challenge is an opportunity to test and improve your models on real-world microscopy data, and support FAIR, open science while doing it.

Get started now!

👉 https://ai4life-mdc25.grand-challenge.org/

 

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Release of the New BioImage Model Zoo Website

Release of the New BioImage Model Zoo Website

The BioImage Model Zoo unveils a complete redesign—offering a more collaborative and interactive platform for the community, with rapid model testing feedback to enhance the contributor experience.

Built as part of the AI4Life project, the revamped platform emerged from the community feedback focused on serving the research community. Early user input revealed that contributor experience is key to enabling greater model ingestion and community engagement. We therefore rebuilt the website from the ground up, focusing on streamlined uploading, testing, validation, and review workflows powered by scalable infrastructure.

What’s new?

The new website comes with a bunch of new features:

  • Complete frontend rewrite with enhanced user experience and more powerful model search capabilities.
  • Intuitive model uploader with in-browser file editing support, streamlining the contribution process.
  • Instant model testing during the upload process, providing immediate feedback and enhanced quality control for contributors.
  • Collaborative review interface allowing reviewers to quickly edit, review, and approve models with real-time collaboration tools.
  • New Hypha-powered backend featuring advanced artifact management, user login, permission systems, real-time collaboration, and comprehensive APIs for programmatic access—enabling seamless model access and upload.
  • BioEngine server infrastructure powered by Ray, enabling robust model test runs and scalable compute resources. It also allows on-premise deployment with an intuitive wizard for easy setup.
  • Resilient storage infrastructure backed by S3 storage hosted at EMBL-EBI with automatic Zenodo backup for long-term preservation.

These upgrades transform the entire model sharing experience, whether you’re contributing cutting-edge models, discovering tools for your research, or collaborating with the community to advance reproducible AI in bioimaging. Looking ahead, this new foundation enables us to make AI models even more accessible through intuitive web applications and AI Agents, designed for federated deployment across institutions to bring powerful bioimaging AI directly to researchers worldwide.

Explore the new platform: https://bioimage.io

Behind the scenes: The rebuilding process was supercharged by the emergence of AI coding assistants—tools enabled rapid frontend development and backend integration, showcasing how “vibe coding” can accelerate scientific software development.

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The AI4Life Challenge #2 is now live

The AI4Life Challenge #2 is now live

We’re happy to announce the second AI4Life Challenge, this time focused on supervised denoising of microscopy images, a key task in enhancing image quality for biomedical research.

Hosted on the Grand Challenge platform, we invite researchers, developers, and enthusiasts to develop solutions for image restoration using deep learning.

Participants will:

  • Access paired datasets of noisy and clean images.
  • Develop and submit models for data-driven, content-aware denoising.
  • Be evaluated against ground truth for quantitative performance.
  • Contribute their solutions to a core task in bioimage analysis.

Whether you’re a seasoned researcher or new to the field, this challenge is an opportunity to test and improve your models on real-world microscopy data, and support FAIR, open science while doing it.

Get started now!

👉 https://ai4life-mdc25.grand-challenge.org/

 

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The AI4Life Model Evaluation Platform: building bridges between the BioImage Archive and the BioImage Model Zoo

The AI4Life Model Evaluation Platform: building bridges between the BioImage Archive and the BioImage Model Zoo

by Teresa Zulueta-Coarasa

The growing number of  models available at the BioImage Model Zoo, are a valuable resource for life scientists aiming to analyze complex imaging data using AI. However, users can struggle to identify which model best matches their own data. At the same time, model developers frequently lack access to diverse datasets, limiting their ability to evaluate how well their models generalize across different biological imaging scenarios.

To address both challenges, we have developed the Model Evaluation Platform which provides side-by-side performance comparisons of different models on a wide variety of imaging datasets from the BioImage Archive.

 

The platform offers two complementary ways to explore model performance. In the model-centric view (“Model Evaluation Platform”), you can compare how a single model performs across a wide range of datasets. This is especially useful for model developers, who can assess how their contributions to the BioImage Model Zoo generalise across diverse imaging data. Alternatively, the dataset-centric view (“Analysed Datasets”) allows you to examine how different models perform on the same dataset. This perspective is ideal for life scientists, who can identify datasets similar to their own and discover which models are likely to yield the most relevant results for their research. This dual approach makes it easier for both scientists and developers to identify optimal use cases for each model.

When you navigate to either the “Analysed Datasets” or the “Model Evaluation Platform”, you will find the models or datasets available within that collection, helping you quickly understand what’s included. From there, you can drill down into individual model or dataset views, which offer more detailed summaries.

On datasets with available ground truth annotations, you will be able to find metrics—such as precision, recall, Intersection over Union (IoU), and Dice score— that you can use to compare the performance of models.

These pages also feature graphical visualisations of model inputs, outputs, and ground truth references—the data used to compute the numerical scores. Input and output images are also available for datasets that do not have ground truth annotations, allowing you to visually inspect the performance of the models.

This Model Evaluation Platform is the first step to achieve our long-term goal to continually increase the utility of the Model Zoo by supporting end-users in selecting appropriate models and enabling developers to benchmark their models against current state-of-the-art approaches with ease.



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AI4Life Community Event Celebrating AI Innovation in Life Sciences held in Helsinki

AI4Life Community Event Celebrating AI Innovation in Life Sciences held in Helsinki

The AI4Life project organised a community event on May 27–28, 2025, at Katajanokan Kasino in Helsinki. 

AI4Life brings together researchers, developers, infrastructure providers, and industry stakeholders from across Europe to accelerate the use of AI in biological and biomedical imaging. Over the course of two days, the event showcased cutting-edge AI tools, impactful research collaborations, and the vibrant community that AI4Life has fostered. 

Highlights included interactive workshops for both life scientists and AI developers, offering hands-on sessions with the BioImage Model Zoo (BMZ)—a platform hosting ready-to-use pre-trained AI models for microscopy image analysis. Attendees explored how to make image data and annotations standardised and FAIR (Findable, Accessible, Interoperable, and Reusable). The BioImage Archive hosts AI-ready datasets that have been benchmarked using AI models from the BMZ. On the infrastructure side, BioEngine was shown to deploy AI models on-premise.

The event also brought together experts from other communities that AI4Life collaborates with, such as the Global BioImage Analysts’ Society  (GloBIAS), in which several AI4Life members are volunteers, and AI4EOSC, a European project that has worked in the interoperability of AI models and that is hosting the AI4Life Cloud Marketplace.

Participants also learned about real-world applications from the AI4Life open calls use cases, such as 3D electron microscopy for plankton research and resolution-enhancement techniques in intravital microscopy. The Finnish Advanced Microscopy Node (FiAM) also promoted its image analysis capabilities. Success stories from the industry partner Zeiss and community contributors, like BiaPy, QuPath, and SpotMAX, demonstrated the project’s impact on both academia and industry.

The event concluded with a forward-looking discussion on the strategic role of research infrastructures, with presentations from EMPHASIS ERIC and EU-OPENSCREEN ERIC on the evolving landscape of European Open Science and AI policy. The discussion emphasised AI4Life’s contributions to interoperability and responsible data sharing.

Funded by the European Union’s Horizon Europe research and innovation programme (grant agreement no. 101057970), AI4Life has laid essential foundations for the future of open, AI-driven science. The tools, datasets, and knowledge produced by AI4Life will continue to benefit the global research community via platforms like the BioImage Model Zoo and the BioImage Archive.

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The BioImage Model Zoo meets the Galaxy platform

The BioImage Model Zoo meets the Galaxy platform

A new Galaxy integration enables researchers to access AI models from the BioImage Model Zoo (BioImage.IO). This integration bridges open-source AI models and an accessible, workflow-based computational environment, allowing scientists across disciplines to include deep learning models in their workflows.

This integration is a leap forward for the AI4Life project, which aims to democratize AI in life sciences. “We’re broadening our focus to engage a wider community of users by expanding the reach of the BioImage.IO Model Zoo,” explains Diana Chiang Jurado, who, together with Leonid Kostrykin, developed a dedicated tutorial to support users. “Using the Galaxy infrastructure, we’re not only lowering technical barriers but also making sure that researchers with no access to local computational resources can still run their analyses.”

Galaxy’s established ecosystem brings some benefits: cloud-based computing, reproducibility through workflow provenance, and a collaborative environment thousands of scientists use worldwide.

Integrating these models wasn’t without its challenges. “The BioImage Model Zoo hosts models built on different architectures and frameworks,” notes Anup Kumar, the leading developer behind the integration. “One of the most exciting outcomes,” Anup adds, “is the accessibility it provides. Scientists who don’t have a background in AI, or who work at underfunded institutions, can now use powerful models with just a few clicks. That’s the kind of impact we’re aiming for.”

With AI-driven image analysis becoming increasingly relevant across scientific fields, from biology to earth sciences or astronomy, the integration sets the stage for new cross-disciplinary collaborations like those outlined in the OSCARS-FIESTA project. “Workflows created in Galaxy are easy to share and adapt,” says Leonid. “That means a model trained on biological data could inspire solutions in climate science or vice versa. It’s about breaking down silos.”

By combining rich model repositories with user-friendly, FAIR workflows, this integration makes advanced image analysis more approachable, adaptable, and impactful for scientists everywhere.

Try the tutorial developed by Diana and Leonid here: Using BioImage.IO models for image analysis in Galaxy.

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AI4Life Workshop at the Euro-BioImaging All Hands Meeting 2025

AI4Life Workshop at the Euro-BioImaging All Hands Meeting 2025

On March 25, 2025, AI4Life hosted a dedicated workshop at the Euro-BioImaging All Hands meeting to strengthen collaboration between AI4Life and the Euro-BioImaging Nodes. The session brought together imaging experts, bioimaging facility representatives, and AI researchers to explore how AI-powered tools can enhance image analysis workflows and identify opportunities for joint initiatives.

Objectives

The workshop aimed to:

  • Introduce AI4Life to the Euro-BioImaging Nodes, showcasing its goals, resources, and services.
  • Gather feedback from the Nodes on how AI4Life can further improve its tools and services to better support the bioimaging community.
  • Define actionable collaboration pathways between AI4Life and the Euro-BioImaging Nodes.
  • Establish an ongoing framework for interaction, contribution, and resource exchange.

Discussion

Challenges in Running AI Models Across Different Infrastructures

Participants discussed the difficulties of deploying AI models across various computational infrastructures, such as high-performance computing (HPC) clusters. The importance of standardized environments was highlighted, with Docker containers and tools like DL4MicEverywhere identified as potential solutions to ensure model compatibility across platforms.

Quality Control and Model Validation

Ensuring AI model quality and reliability was a major discussion point. Participants emphasized the need for robust validation processes and user feedback mechanisms to assess model performance. AI4Life aims to incorporate community-driven validation strategies into its workflows.

Integration with the BioImage Archive

The session explored how AI models can be better integrated with the BioImage Archive, focusing on:

  • Programmatic submission of datasets to simplify data sharing for frequent users.
  • Improved interoperability between the BioImage Archive and repositories like EMPIAR, especially for cryo-electron tomography (cryo-ET) data.
  • Standardization of data formats and metadata to enhance AI-readiness and reusability.

Related events and community engagement

The AI4Life team and the Euro-BioImaging community continue to foster dialogue through upcoming events:

Resources

Participants and interested researchers can explore AI4Life’s resources:

Slides

(Pictures by Ayoub El Ghadraoui)

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AI4Life highlighted as a European Commission Success Story

AI4Life highlighted as a European Commission Success Story

The European Commission (EC) Directorate-General for Research and Innovation (DG RTD) has recognised the AI4Life project for its achievements, selecting it as a success story on the DG RTD website. This highlights AI4Life’s relevance and significant impact on AI-powered bioimage analysis in Europe.

Our efforts to make AI-powered bioimage analysis accessible to all, through open calls supporting scientists to community-driven initiatives, have been recognised with this milestone.

This acknowledgement reinforces AI4Life’s role in breaking down barriers to AI adoption and fostering collaboration with initiatives like AI4EOSC to strengthen the EOSC research ecosystem.

Read the full story here: https://projects.research-and-innovation.ec.europa.eu/en/projects/success-stories/all/making-ai-accessible-life-science-research

 

Want to connect with the AI4Life community? Join our upcoming community event.