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Third AI4Life Open Call: Announcement of selected projects

Third AI4Life Open Call: 
Announcement of selected projects

by Florian Jug & Beatriz Serrano-Solano

The third AI4Life open call received 28 applications from diverse scientific disciplines.

We are again distinguishing two phases, a consultancy phase, and a project execution phase. This is to maximize the value of our service to the scientific community by providing feedback (e.g. feasibility, existing tools, approaches we would take) to a larger number of projects, and then taking on a small number of projects for more in-depth support based on informed opinions of experts regarding the readiness of the project (as we also did last year).

The selection of projects, for both phases, was conducted by an international group of project reviewers, similar to the procedure we conducted in OC2.

Projects selected for Phase 1: Project Consultation

1 – Mechanism of action of insecticidal proteins produced by Bacillus thuringiensis bacteria

3 – Improving nuclei segmentation using Cellprofiler and StarDist

10 – Feeding or Fading? Automated image analysis of white corals’ behavior to unlock natural patterns, stress responses, and conservation needs responses, and conservation needs

11 – 3D Matrix Motility Map (3DM³)

14 – The speed of life in trees — linking wood anatomy with wood lifespan and tree growth

17 – Imprints of Wind Disturbances  on Wood Anatomy

18 – Detection of Nuclear Pore Complexes with shape variability imaged by DNA PAINT

19 – Segmentation of sparse bacteria in human tissue

23 – Smart electron tomography data acquisition

24 – Determine the interaction between neutrophils and preneoplastic cells in a zebrafish skin tumour initiation model

25 – Scalable high-dimensional subcellular profiling in 3D

26 – Automatic microtubule doublet picking in tomograms

27 – 3D cell type identification in lung organoids

Please note that projects with IDs 1 and 24 did not respond to our invitation for consultation even after multiple reminders. Hence, we conducted consultations on a total of 10 projects.

Consultation sessions were typically conducted with between three to five AI4Life experts and consisted of a one-hour discussion during which the experts could ask additional questions regarding the project and then provide guidance regarding the feasibility of the respective projects. Along with this kind of “reality check”, the experts also aimed at giving tangible advice on existing tools and methods that might help the consulted researchers in improving their workflows and achieving the desired analysis results.

Projects selected for Phase 2: Deep Learning project support

With the additional information from the consultancy phase at hand, the group of AI4Life experts came together and discussed the most promising projects to be taken on in Phase 2 (deep-learning support). As last year, this decision was guided by a combination of project feasibility, readiness, re-usability by the community, and how well each project fits within the experiences and capabilities of the AI4Life team itself. These considerations were combined with the intention to maximize the utility and impact on the bioimage analysis community of this Open Call.

3 – Improving nuclei segmentation using Cellprofiler and StarDist

10 – Feeding or Fading? Automated image analysis of white corals’ behavior to unlock natural patterns, stress responses, and conservation needs responses, and conservation needs

11 – 3D Matrix Motility Map (3DM³)

14 – The speed of life in trees — linking wood anatomy with wood lifespan and tree growth

17 – Imprints of Wind Disturbances  on Wood Anatomy

18 – Detection of Nuclear Pore Complexes with shape variability imaged by DNA PAINT

19 – Segmentation of sparse bacteria in human tissue

26 – Automatic microtubule doublet picking in tomograms

Who was involved in the process?

In contrast to last year, we conducted the pre-screening and selection for the consultancy phase within AI4Life and did not include external reviewers.

If you missed our open calls and would like to benefit from a similar service, we recommend Euro-BioImaging’s Web Portal, where you can access a network of experts in the field of image analysis. You can also find a bioimage analyst in your geographic and scientific area in the GloBIAS database of bioimage analysts. Please note that this service may involve associated costs, but access funds for certain research topics are available through some initiatives. We and Euro-Bioimaging will be happy to help you find suitable opportunities!

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AI4Life: Advancing AI Adoption in Life Sciences

AI4Life: Advancing AI Adoption in Life Sciences

AI4Life has been working alongside Life Science Research Infrastructures (LS RIs) to address the growing need for AI-driven bioimage analysis. Through a combination of outreach, community engagement, hackathons, and strategic collaborations, the project has significantly expanded access to AI tools for researchers worldwide.

Key achievements

Outreach and Dissemination

AI4Life partners and collaborators have engaged in extensive dissemination activities (175 by mid-February 2025) to promote the project’s services and opportunities, reaching diverse audiences across multiple countries and scientific communities. These activities have taken various forms, including conferences, training events, and scientific meetings, ensuring broad engagement with researchers, industry professionals, and the wider life sciences community. 

The activity types were mostly scientific conferences, followed closely by education/training events (Figure 1).

Conferences provided AI4Life with a platform to showcase the project through presentations and posters. At the same time, training initiatives played an essential role in building community around the BioImage Archive (BIA) and the BioImage Model Zoo (BMZ). Additionally, the project established collaborations with EU-funded projects and other scientific initiatives to strengthen AI4Life’s impact and sustainability.

The geographical distribution of the dissemination activities (Figure 2) highlights a strong international presence even beyond the European scope, together with numerous online events, which enabled global reach and accessibility.

Figure 1. Outreach and dissemination activities from the beginning of the project in September 2022 until February 2025 by type of activity.

Figure 2. Outreach and dissemination activities from the beginning of the project in September 2022 until February 2025 by location.

Community Engagement

AI4Life has organised and contributed to multiple workshops and training events to introduce AI methods to the life science community. These events often included presentations and hands-on sessions by AI4Life experts.

As part of its outreach efforts, AI4Life has collaborated closely with Life Science Research Infrastructures (LS RIs) through Work Package 7, engaging with partners at Euro-BioImaging, EU-OPENSCREEN, the European Marine Biological Resource Centre (EMBRC), EMPHASIS, and Instruct-ERIC. Through monthly meetings, AI4Life has raised awareness among the partner LS RIs about the opportunities it offers to their scientific communities while gaining insight into their specific needs. This ongoing dialogue has enabled AI4Life to contribute meaningfully to LS RI-led initiatives, ensuring its presence at relevant events. A key example is the EU-OPENSCREEN Autumn Training School, where AI4Life members showcased the offered services to demonstrate their impact on bioimage analysis.

Hackathons

Hackathons have been essential to AI4Life, bringing together project members and key stakeholders to develop new features and enhance existing services (Figure 3).

These events also fostered collaboration by inviting industry partners, community partners and participants from other initiatives to contribute their knowledge. Community partners typically include organisations, companies, research groups, or software teams that can consume and/or produce resources of the BioImage Model Zoo. By involving them in shared development activities, AI4Life ensures interoperability across resources.

We strategically aligned these events with General Assemblies to maximise impact and efficiency, leveraging attendees’ expertise while minimising travel requirements.

Figure 3. Some of the Hackathons organised by AI4Life.

Strategic Collaborations and Recognition

AI4Life has strategically partnered with other EU-funded projects to increase the project’s sustainability and impact. The European Commission (EC) Directorate-General (DG) for Research and Innovation has acknowledged the project’s achievements and selected AI4Life to be showcased at the European Research and Innovation Days 2024 and as a success story on the DG Research and Innovation website.

As AI4Life moves into 2025, the project remains committed to expanding its outreach, further developing AI tools, and continuing international collaborations. By maintaining strong engagement with the life sciences community, AI4Life will ensure that its contributions to bioimage analysis remain at the forefront of scientific innovation.

For more information on AI4Life’s activities, read the deliverable D7.2 available in Zenodo and for upcoming events, visit the events section: https://ai4life.eurobioimaging.eu/events.

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Expanding AI4Life training resources: New and updated materials

Expanding AI4Life Training Resources: New and Updated Materials

by Caterina Fuster-Barceló

To enhance accessibility and usability, the European consortium AI4Life has expanded and updated the BioImage Model Zoo (BMZ, BioImage.IO) training materials as part of the D3.3 Deliverable (available in zenodo). This effort involved evaluating existing resources, introducing new materials, and updating documentation to ensure comprehensive guidance for both developers and end-users of the BMZ.

Currently, the AI4Life training material list includes over 30 different training materials, spanning website documentation, case studies, video tutorials, computational notebooks, and slides. The majority of resources are targeted at end-users (47.1%) and developers (32.4%), with additional materials designed to serve both audiences (20.6%). The updated distribution of training materials and their target audiences is visualized in the figures below.

Key Insights from the Updated Training Materials

  • Video tutorials (14) remain the most used format, followed by website documentation (9) and computational notebooks (6).
  • Nearly half of the training materials cater to end-users, helping them navigate BMZ and integrate AI models into workflows.

Training Documentation Aligned with the AI Model Lifecycle

To ensure that documentation is structured in a way that is intuitive and practical for users, we have aligned its training materials with the lifecycle of a Deep Learning (DL) model in the BMZ. The image below illustrates this workflow-based documentation approach, categorizing resources according to the key stages of models and the targeted users of each case.

Each stage of the DL model lifecycle—model development, packaging, upload, accessibility, deployment, and fine-tuning—is supported by a combination of website documentation, video tutorials, computational notebooks, and case studies. Developer-focused documentation (highlighted in orange) ensures that DL models are properly packaged, documented, and submitted to BMZ, while end-user documentation (highlighted in blue) provides guidance on how to access and integrate models into image analysis workflows.

Highlighted Updates and New Materials

1. New Guided Tutorials on YouTube. AI4Life has added three new step-by-step video tutorials to help users and developers navigate BMZ more efficiently.

2. Comprehensive Guide on Running BMZ Models in Community Partner Tools. A new documentation resource provides clear, step-by-step instructions on how to execute BMZ models across different bioimage analysis software.

3. Expanded Documentation on the Resource Description File (RDF). To improve model documentation clarity, AI4Life has introduced updated materials explaining the BMZ Resource Description File (RDF) in various formats, making it more accessible to different user groups.

These updates strengthen AI4Life’s commitment to providing high-quality, accessible training materials that support the growth and usability of the BioImage Model Zoo. By continuously refining and expanding these resources, AI4Life ensures that bioimage analysis researchers and developers have the tools they need to efficiently integrate, deploy, and utilize AI models in their scientific workflows.

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AI4Life and AI4EOSC join forces to facilitate AI model deployment for Bioimage Analysis

AI4Life and AI4EOSC join forces to facilitate AI model deployment for Bioimage Analysis

AI4Life and AI4EOSC started an effective collaboration at the AI4Life workshop in Madrid, where AI4EOSC members participated and introduced the AI4OS software stack and the AI platform, which enables training, deployment, sharing and monitoring of AI models. Building on this partnership, AI4EOSC has developed a tailored version of the AI platform specifically for AI4Life, enabling researchers to deploy and run AI models via the AI4Life Cloud Marketplace (Figure 1).

Figure 1. AI4Life Cloud Marketplace accessible at  https://ai4life.cloud.ai4eosc.eu/marketplace

This new platform simplifies the process of launching AI models for bioimaging tasks by integrating with AI4EOSC’s computational infrastructure. Researchers can now take advantage of this scalable and user-friendly environment to run models from the BioImage Model Zoo, enhancing accessibility and reproducibility in bioimage analysis (Figures 2 and 3).  

Figure 2. BioImage Model Zoo models imported to the AI4Life Cloud Marketplace

Figure 3. Example model from BioImage Model Zoo

Currently, the system supports models adhering to the v5 of the BioImage.IO specifications. This serves as a strong example of how FAIR, standardized models pave the way for interoperability, making resources more accessible across different platforms. The success of this integration was highlighted at the EOSC Winter School in Sevilla, where it was showcased as a model case for interoperability between research infrastructures.  

For detailed guidance on deploying models via this platform, refer to the official AI4EOSC dashboard documentation:

https://docs.ai4eosc.eu/en/latest/howtos/deploy/external.html  

This collaboration reinforces AI4Life’s mission to make AI-powered bioimage analysis more accessible, interoperable, and integrated into the European Open Science Cloud (EOSC). Stay tuned for further updates as we continue to advance AI capabilities for the bioimaging community!

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New Community Partner: CAREamics!

New Community Partner: CAREamics!

by Joran Deschamps, Beatriz Serrano-Solano

CAREamics, a library that simplifies using deep-learning denoising algorithms, has joined the BioImage Model Zoo.

With CAREamics, users can improve their analysis pipelines by removing noise from their microscopy images through robust image restoration. By leveraging popular deep learning methods, CAREamics empowers researchers to recover high-quality data from low light or fast imaging,  ensuring lower sample phototoxicity, better quantification, and reproducibility in their experiments.

As a Community Partner, CAREamics now provides ready-to-use models compatible with the BioImage.io model format. 

Discover CAREamics models on the BioImage Model Zoo: https://bioimage.io/#/?partner=careamics&type=application

 

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SpotMax joins the BioImage Model Zoo as a Community Partner

SpotMax joins the BioImage Model Zoo as a Community Partner

by Beatriz Serrano-Solano

We are excited to announce that SpotMax, the advanced image analysis framework, has officially joined the BioImage Model Zoo as a Community Partner! This collaboration allows users to leverage SpotMax for running BioImage.io models, further enhancing its integration into the growing bioimage analysis ecosystem.

SpotMax is renowned for its powerful capabilities in automated spot detection and quantification of microscopy images. With a user-friendly interface and built-in features for cell segmentation and tracking, SpotMax enables researchers to harness the full potential of multi-dimensional datasets.

As part of this partnership, SpotMax now provides ready-to-use models and workflows fully compatible with the BioImage.io model format. These tools are readily accessible through the BioImage Model Zoo, empowering researchers to integrate SpotMax models into their analysis pipelines.

This integration underscores our commitment to fostering collaboration and accessibility in bioimage analysis. By bringing SpotMax into the BioImage Model Zoo, we aim to provide the research community with a richer, more versatile toolkit for tackling diverse challenges in bioimaging.

We are delighted to welcome SpotMax as a Community Partner and can’t wait to see the transformative impact of this partnership. Explore SpotMax models on the BioImage Model Zoo today!

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AI4Life at SPAOM 2024: Bridging Advanced Microscopy and AI-Driven Image Analysis

AI4Life at SPAOM 2024: Bridging Advanced Microscopy and AI-Driven Image Analysis

by Caterina Fuster-Barceló

The Spanish-Portuguese Advanced Optical Microscopy Meeting (SPAOM) 2024 brought together leading experts in optical microscopy and image analysis to foster collaboration and innovation in the field. Held this year in the historic city of Toledo, in the Castilla-La Mancha region, SPAOM once again proved to be a dynamic platform for knowledge exchange, networking, and showcasing cutting-edge advancements.


AI4Life was represented by Arrate Muñoz-Barrutia and Caterina Fuster-Barceló, who played a pivotal role in the event. They hosted an engaging Image Analysis Forum, introducing AI4Life and the BioImage Model Zoo, two initiatives revolutionising the intersection of artificial intelligence and bioimaging. During the forum, participants also learned about the BioImage.IO Chatbot and the BioImage Archive, which are closely linked to AI4Life’s mission to advance open and accessible bioimage analysis tools and datasets.

 

Beyond the forum, Arrate and Caterina immersed themselves in the vibrant SPAOM community, networking with researchers and discovering the remarkable projects spearheaded by other attendees. Their participation highlighted AI4Life’s commitment to fostering collaboration and pushing the boundaries of AI applications in bioimaging.

We are excited to see how the connections and ideas sparked at SPAOM 2024 will continue to shape the future of advanced microscopy and image analysis. Stay tuned for updates on AI4Life’s next steps!

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AI4Life’s Highlights at I2K 2024

AI4Life’s highlights at I2K 2024

by Beatriz Serrano-Solano

AI4Life played an important role at the 2024 Images to Knowledge (I2K) conference, held from October 23-25 at the Human Technopole in Milan. As a major event for experts at the intersection of life sciences and quantitative image analysis, I2K offered AI4Life partners an ideal platform to showcase their latest advancements and community contributions.

AI4Life’s impact began at the organizational level, with the AI4Life scientific coordinators Florian Jug and Anna Kreshuk serving on the scientific organizers committee. The conference also welcomed Ricardo Henriques from Instituto Gulbenkian de Ciência (IGC) – University College London (UCL) as an invited speaker, highlighting AI4Life’s expertise in superresolution microscopy.

IMG_20241025_235937
Hands-on learning with specialised workshops

A series of workshops allowed participants to dive into AI4Life’s practical tools and methods:

  • Building Your Own Chatbot for BioImage Analysis: Opening the series on Wednesday.
  • Accelerating Microscopy Image Annotation with SAMJ Annotator and DL4MicEverywhere: Making your deep learning pipelines flexible, shareable, and reproducible workshops on Thursday, enhancing image annotation speed and flexibility.
  • Friday’s sessions included 4 workshops on BiaPy: deep learning based Bioimage Analysis for all audiences, Recent Advances in ilastik, AI4Life: Empowering BioImaging through the BioEngine, and an Introduction to AI4Life and the BioImage Model Zoo
Poster presentations

AI4Life partners presented two posters:
– BioImage.IO Chatbot: A community-driven AI assistant advancing integrative computational Bioimaging, presented on Wednesday.
– Building Foundations for AI-Driven Bioimage Analysis: Discussing infrastructure and annotation platforms critical for bioimage analysis, shared on Thursday. 

 

We look forward to the next I2K and are excited to contribute further to this inspiring community!

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Summary of the AI4Life 3rd General Assembly

Summary of the AI4Life 3rd General Assembly

by Beatriz Serrano-Solano

The AI4Life 3rd General Assembly, held at Human Technopole in Milan on October 21-22, 2024, brought together project partners and collaborators to discuss advancements, challenges, and future directions in AI-driven image analysis.

Key Highlights

The event started with the welcoming remarks by Dorothea Dörr (AI4Life Project Manager) and John Eriksson (Euro-BioImaging Director General and coordinator of the project), followed by an introductory reflection on AI4Life’s achievements over the past two years by Anna Kreshuk and Florian Jug, scientific coordinators of the project.

 
Session 1 – AI4Life services

The first session focused on demonstrations for creating, uploading, and consuming models.
* Demo 1: Showcased how users can create and upload models via a web interface or programmatically through an API.
* Demo 2: Demonstrated model consumption from bioimage.io, community partner tools, and code environments.
* Demo 3: Emphasized the importance of FAIR principles, highlighting AI-ready datasets, FAIR models, and documentation enhancements.

Session 2 – Work Package updates

Work package leads updated the attendees on infrastructure and user services, with WP6 announcing that AI4Life’s 3rd Open Call will be launched soon.

Session 3 – Additional Work Package Presentations

Presentations from WPs 1, 3, 5, and 7 covering project coordination, direct support, data standards, and outreach.

Research Infrastructure Updates

Brief updates from the partner Research Infrastructures: INSTRUCT-ERIC, EU-OPENSCREEN ERIC, EMPHASIS, and EMBRC-ERIC.

Session 4 – Planning the Future

A forward-looking session exploring upcoming project collaborations with BigPicture and AI4EOSC, along with a panel discussion on project sustainability and post-project impact.


The assembly concluded with plans for year three’s demos, events, and outreach initiatives, with final remarks from Anna Kreshuk and Florian Jug.

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AI4Life in Chemical & Engineering News

AI4Life in Chemical & Engineering News

by Beatriz Serrano-Solano

In October 2024, Chemical & Engineering News explored how artificial intelligence is revolutionizing microscopy and featured AI4Life’s role in bridging the gap between life scientists and AI experts. AI4Life was recognized for making advanced AI tools more accessible to researchers, supporting their diverse needs, and fostering collaboration across disciplines.

Read the full article here.