Etiological diagnosis of dementia using federated AI

I was awarded the Erasmus MC fellowship 2022, check out the project video here!

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Dr. Esther Bron leads the research line ‘Neuroimage analysis and Machine learning’ at the department of Radiology & Nuclear Medicine of Erasmus MC – University Medical Center Rotterdam , the Netherlands. In 2011, Esther completed her master’s degree in medical physics at VU Amsterdam (cum laude). She defended her doctoral thesis in 2016 at the Erasmus University Rotterdam, which focused on the development and validation of advanced image analysis techniques for computer-aided diagnosis of dementia. Since then, Esther has successfully built her research line and has been appointed assistant professor in 2020. Esther is also Image Data Coordinator at Health-RI since 2022.

Research projects

Dr. Esther Bron is PI in several multicenter projects (CVON Heart Brain Connection, ImProVas, Parelsnoer), and coordinator of the Image & Data Science Platform. She won the Young eScientist Award 2018 by the Netherlands eScience Center and was finalist for the Young Outstanding Researcher Award 2020 by Alzheimer Nederland. Recent research outcomes of Esther Brons team include development of DEBM – a novel disease progression model, which is currently being evaluated in several neurodegenerative diseases (i.e. Alzheimer’s disease, frontotemporal dementia, Creutzfeldt-Jacob’s disease, multiple sclerosis). In addition, the team developed neural network approaches for cross-sectional and longitudinal white matter tract segmentation. Esther (co-)organized two international grand challenges for the objective comparison of algorithms for AD diagnosis (CADDementia challenge) and prediction (TADPOLE challenge).

International collaborations

Dr. Esther Bron collaborates with clinicians, companies, and several national and international research groups. A major partner is the Progression Of Neurodegenerative Disease (POND) group at University College London, with whom she organized the TADPOLE challenge which evaluates which data, processing pipelines, and predictive models provide the best estimates of future AD progression. In 2018, Esther won the Young eScientist Award which led to a collaboration with the Netherlands eScience Center and several international research teams (e.g. National University of Singapore, Tecnologico de Monterrey) on the TADPOLE-SHARE project.


Esther also works for Health-RI as the Imaging Data Coordinator, part of the Architecture team. The mission of Health-RI data is to achieve better health for citizens and patients by reusing health data with an integrated health data infrastructure for research and innovation. Esther is setting up the Imaging Data working group to integrate the infrastructure for finding, getting and using Imaging Data with the eight Health-RI nodes in the Netherlands.

PhD degree

Esther obtained her PhD degree on March 9 2016 with her thesis entitled Advanced MRI Analysis for Computer-Aided Diagnosis of Dementia. In her PhD research under supervision of Prof. W.J. Niessen, dr. S. Klein and dr. M. Smits, she developed and improved multiple methods for computer-aided diagnosis of dementia. As part of this, she organized the CADDementia challenge which compared image-based diagnosis algorithms. In the CADDementia workshop at the MICCAI 2014 conference, she presented the results of this challenge. With this project, Esther was nominated for the Dutch Data Prize 2014 and won a Best Scientific Paper Presentation Award at the European Congress of Radiology in 2015.


Erasmus MC - University Medical Center Rotterdam

Department of Radiology & Nuclear Medicine
Office Na2506
P.O. Box 2040
3000 CA Rotterdam, NL


1. Journal Papers

2. Conference Papers

3. Book Chapters

4. Registers

5. Conference Proceedings

6. Conference Abstracts

  • A. S. Alic, D. Arce Grilo, M. Birhanu, E.E. Bron, V. Kalokyri, T. Kussel, K. Lang, K. Majcen, I. Blanquer, Early platform release of the federated European Cancer Imaging Infrastructure, European Conference on Radiology, 2024
  • P. Mateus, J. Yu, S. Garst, A. Harms, D. Cats, I. Bermejo, G. Roshchupkin, H. Mei, E.E. Bron, Federated learning for brain age prediction in three population-based MRI cohorts, European Conference on Radiology, 2024
  • P. Mateus, J. Yu, S. Garst, A. Harms, D. Cats, I. Bermejo, G. Roshchupkin, M. Reinders, P.E. Slagboom, H. Mei, E.E. Bron, Federated BrainAge estimation from MRI: a proof of concept, Alzheimer’s Association International Conference (AAIC), 2023
  • M.F. van Haaften, J. Yu, H. Seelaar, M.W Vernooij, E.E. Bron , The influence of vascular pathology on the generalizability of machine learning models for cognitive performance estimation, Alzheimer’s Association International Conference (AAIC), 2023
  • J.M. Poos, L.D.M. Grandpierre, E.L. van der Ende, J.L. Panman, J.M. Papma, H. Seelaar, E. van den Berg, R. van ’t Klooster, E.E. Bron, R.M.E. Steketee, M.W.e Vernooij, Y.A.L. Pijnenburg, S.A.R.B. Rombouts, J.C. van Swieten, L.C. Jiskoot, Longitudinal brain atrophy rates in presymptomatic genetic frontotemporal dementia, Alzheimer’s Association International Conference (AAIC), 2022

7. Invited Lectures

  • E.E. Bron, Data curation for machine learning: What we can do together - federated learning, Annual meeting of European Society of Magnetic Resonance in Medicine and Biology (ESMRMB), 07-10-2023
  • E.E. Bron, Bias in image-based machine learning of dementia causing diseases, NIAS Expert Workshop For Women in Science - Disease heterogeneity in dementia causing disease, 13-07-2023
  • E.E. Bron, Sharing images: The why and how of medical imaging analysis research, Data stewards course - Health-RI, 03-07-2023
  • E.E. Bron, Datasets for medical image challenges Image analysis and machine learning competitions in dementia , Webinar series: Datasets trough the looking glass, 05-06-2023
  • E.E. Bron, The why, what and how of federated learning: The Netherlands Consortium of Dementia Cohorts (NCDC), SURF seminar: Your secrets are safe with us: Tooling for research with sensitive data, 21-03-2023


  • Esther Bron, PhD

    Assistant Professor

  • Hyunho Mo
    Hyunho Mo, PhD

    Postdoctoral researcher

  • Bo Li
    Bo Li

    PhD Candidate / Postdoctoral researcher

  • Wenjie Kang

    PhD Candidate

  • Myrthe van Haaften

    PhD Candidate

  • Kaouther Mouheb

    PhD Candidate

  • Jing Yu
    Jing Yu

    Affiliated PhD Candidate

  • Alexander Harms

    Research Software Engineer

  • Picture of team member Mahlet Birhanu
    Mahlet Birhanu

    Research Software Engineer

  • Sönke van Loh

    MSc Electrical Engineering, U Twente

  • Nathalie Koorn

    MSc Biomedical Engineering, TU Delft

  • Interested in joining?

    See MSc projects here!

Former team members

  • Claudia Chinea Hammecher

    MSc Biomedical Engineering, TU Delft (2023)

  • Myrthe van Haaften

    MSc Technical Medicine, EMC/TU Delft/LUMC (2022)

  • Karina Hoefnagel

    BSc Psychobiology, Universiteit van Amsterdam (2022)

  • Dalia Aljawaheri

    MSc Nanobiology, TU Delft (2021)

  • Vikram Venkatraghavan

    Postdoctoral researcher / PhD candidate (2021)

  • Diede Wijnbergen

    MSc Technical Medicine, EMC/TU Delft/LUMC (2021)

  • Lotte Mulder

    MSc Biomedical Engineering, TU Delft (2021)

  • Liselot Goris

    MSc Biomedical Engineering, U Twente (2021)

  • Niels de Bruin

    MSc Computer Science, TU Delft (2020)

  • Thomas Michelotti

    MSc Econometrics, EUR (2020)

  • Savine Martens

    MSc Biomedical Engineering, TU Delft (2020)

  • Theodoros Theodoridis

    MSc Medical Natural Sciences, VU (2019)

  • Marloes Adank

    MSc Biomedical Engineering, TU Delft (2019)

  • Jara Linders

    MSc Brain & Cognitive Sciences, UvA (2019)

  • Leontine Ham

    MSc Medical Natural Sciences, VU (2018)

  • Thomas Michaud

    MSc, ENSEEIHT Toulouse (2018)

  • Sara Laros

    BSc Medical Natural Sciences, VU (2018)

  • Jim Smit

    BSc Clinical Technology, EMC/TU Delft/LUMC (2017)

  • Sandrine Lacomme

    MSc, ENSEEIHT Toulouse (2013)

  • Ines Merida

    MSc, Polytech Marseille (2012)