Esther E. Bron, PhD - Neuroimage analysis & Machine learning

Etiological diagnosis of dementia using federated AI

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

Key publications

E.E. Bron et al. Ten years of image analysis and machine learning competitions in dementia, NeuroImage, 2022
E.E. Bron et al. Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer’s Disease , NeuroImage: Clinical, 2021
B. Li et al. Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration, NeuroImage (Special issue on Longitudinal Neuroimaging), 2021
V. Venkatraghavan et al. Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations, NeuroImage, 2021

Dr. Esther Bron is Assistant Professor in Neuroimage analysis and Machine learning. She holds a PI position at the department of Radiology and Nuclear Medicine of the Erasmus MC – University Medical Center Rotterdam , the Netherlands. Esther is also Image Data Coordinator at Health-RI. Her mission is to extend the value of big data and artificial intelligence (AI) in the neurology domain to the benefit of patients and doctors.

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.

Health-RI

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.

Contact

Erasmus MC - University Medical Center Rotterdam

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

Awards

eScience Center
Young eScientist Award 2018

More information
European Congress of Radiology
Best Scientific Paper Presentation Award 2015 "Computer Applications"

In the press

Publications

1. Journal Papers

M. van Dinther, A.M. Hooghiemstra, E.E. Bron, A. Versteeg, A.E. Leeuwis, T. Kalay, J.E. Moonen, W.H. Backes, J.F.A. Jansen, L.J. Kapelle, M.J.P. van Osch, M.J.A.P. Daemen, G.J. Biessels, J. Staals, Heart-Brain Connection consortium, R.J. van Oostenbrugge. , Lower cerebral blood flow predicts cognitive decline in patients with vascular cognitive impairment, Alzheimer's & Dementia, 2023
M.J. Leming, E.E. Bron, R. Bruffaerts, Y. Ou, J.E. Iglesias, R.L. Gollub, and H. Im, Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting, npj Digital Medicine, 2023
L.M. Overmars*, S. Kuipers*, B. van Es, J. de Bresser, E.E. Bron, I.E. Hoefer, W.W. van Solinge, L.J. Kappelle, M.J.P. van Osch, C.E. Teunissen, G.J. Biessels**, S. Haitjema**, Heart-Brain Connection Consortium, A cluster of blood-based protein biomarkers associated with decreased cerebral blood flow relates to future cardiovascular events in patients with cardiovascular disease, Journal of Cerebral Blood Flow and Metabolism, 2023
S. Kuipers, S.W. Willemse, J.P. Greving, E.E. Bron, R.J. van Oostenbrugge, M.J.P. van Osch, G.J. Biessels, L.J. Kappelle, Heart-Brain Connection Consortium. , Lower Haemoglobin Concentrations are Associated with Impaired Cognition in Patients with Carotid Artery Occlusion , Cerebral Circulation - Cognition and Behavior, 2023
R. Pascuzzo, V. Venkatraghavan, E.E. Bron, M. Moscatelli, M. Grisoli, A. Pickens, M.L. Cohen, L.B. Schonberger, P. Gambetti, B.S. Appleby, S. Klein, A. Bizzi, A discriminative event-based model for subtype diagnosis of sporadic Creutzfeldt-Jakob disease using brain MRI, Alzheimer's & Dementia, 2023

2. Conference Papers

W, Kang, B. Li, J.M. Papma, L.C. Jiskoot, P.P. De Deyn, G.J. Biessels, J.A.H.R. Claassen, H.A.M. Middelkoop, W.M. van der Flier, I.H.G.B. Ramakers, S. Klein, E.E. Bron, An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease, Clinically-oriented and Responsible AI for Medical Data Analysis (Care-AI) - MICCAI workshop, 2023
X. Liu, B. Li, E.E. Bron, W.J. Niessen, E. Wolvius, G. Roshchupkin, Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis, 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021
B. Li, W.J. Niessen, S. Klein, M.A. Ikram, M.W. Vernooij, and E.E. Bron, Learning unbiased registration and joint segmentation: evaluation on longitudinal diffusion MRI, SPIE Medical Imaging - Image Processing (oral presentation), 2021
B. Li, W.J. Niessen, S. Klein, M. de Groot, M.A. Ikram, M.W. Vernooij, E.E. Bron, A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (oral presentation), 2019
R.V. Marinescu, N.P. Oxtoby, A.L. Young, E.E. Bron, A.W. Toga, M.W. Weiner, F. Barkho, N.C. Fox, P. Golland, S. Klein, and D.C. Alexander, TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data, Second MICCAI workshop on “PRedictive Intelligence in MEdicine” (PRIME) , 2019

V. Venkatraghavan, F. Dubost, E.E. Bron, W.J. Niessen, M. de Bruijne, and S. Klein, Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia, International conference on Information Processing in Medical Imaging (IPMI), 2019
B. Li, M. de Groot, M.W. Vernooij, M.A. Ikram, W.J. Niessen, and E.E. Bron, Reproducible White Matter Tract Segmentation using a 3D U-Net on a Large-scale DTI Dataset, Machine Learning in Medical Imaging, 2018
V. Venkatraghavan, E.E. Bron, W.J. Niessen and S. Klein, A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling, International conference on Information Processing in Medical Imaging (IPMI), 2017
W. Huizinga, D.H.J. Poot, G. Roshchupkin, E.E. Bron, M.A. Ikram, M.W. Vernooij, D. Rueckert, W.J. Niessen and S. Klein, Modeling the brain morphology distribution in the general aging population, Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2016
E.E. Bron, M. Smits, J.C. van Swieten, W.J. Niessen and S. Klein, Feature Selection Based on SVM Significance Maps for Classification of Dementia, Machine Learning in Medical Imaging 2014, LNCS 8679, 2014
E.E. Bron, R.M.E Steketee, G. Houston, J.C. van Swieten, A. Hammers, W.J. Niessen, M. Smits and S. Klein, Classification of early-stage presenile dementia based on arterial spin labeling and structural MRI, MICCAI 2012 Workshop on Novel Imaging Biomarkers for Alzheimer’s Disease and Related Disorders (NIBAD’12), 2012

6. Conference Abstracts

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
B. Li, X. Liu, W.J. Niessen, E. Wolvius, M.W. Vernooij, M.A. Ikram, G.V. Roshchupkin*, E.E. Bron*, A high-resolution autoencoder for construction of interpretable brain MRI endophenotypes, Organization for Human Brain Mapping Annual Meeting (oral presentation), 2022
B. Li, J. de Bresser, W.J. Niessen, M.J.P. van Osch, W.M. van der Flier, G.J. Biessels, M.W. Vernooij, E.E. Bron, Prior-knowledge-informed deep learning for lacune detection andquantification using multi-site MRI, Organization for Human Brain Mapping Annual Meeting, 2022

7. Invited Lectures

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
E.E. Bron, The why, what and how of federated analysis in dementia research, NCDC Winterschool, 20-01-2023
E.E. Bron, Modeling disease progression in genetic frontotemporal dementia, The International Society for Frontotemporal Dementias (ISFTD), 03-11-2022
E.E. Bron, Image analysis and machine learning competitions in dementia , POND2022, Center for Medical Image Computing, University College London, 06-07-2022
E.E. Bron, Clinical validation of commercial automated volumetric MRI tools in the memory clinic, Symposium on Opportunities and Barriers to Neuroimaging-based AI in Memory Clinics for Age-related Dementias, Annual Meeting of the Organization for Human Brain Mapping, 21-06-2022

Team

Esther Bron, PhD

Assistant Professor
Bo Li

Postdoctoral researcher / PhD candidate
Wenjie Kang

PhD Candidate
Myrthe van Haaften

PhD Candidate
Jing Yu

Affiliated PhD Candidate
Alexander Harms

Research Software Engineer
Claudia Chinea Hammecher

MSc Biomedical Engineering, TU Delft
Interested in joining?

See MSc projects here!

Former team members

Myrthe van Haaften
MSc Technical Medicine, EMC/TU Delft/LUMC (2022)
Mahlet Birhanu
Research Software Engineer (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)

Etiological diagnosis of dementia using federated AI

Key publications

Research projects

International collaborations

Health-RI

PhD degree

Contact

Awards

Erasmus MC Fellowship 2022

Winner ASL challenge 2022

VIVA400 Nomination - Healthcare and Education

Finalist Young Outstanding Researcher Award

Winner Alzheimer prediction challenge

In the press

Image data services interview at the Population Imaging Flagship Node Rotterdam

Interview Bo Li "Collaboration ensures that I can do effective research"

Talentprijs: Young Outstanding Researcher Award

Esther Bron: “Uiteindelijk zal dit onderzoek bijdragen aan een medicijn voor alzheimer”

A platform to better predict Alzheimer’s disease

Publications

Advanced MRI analysis for computer-aided diagnosis of dementia

1. Journal Papers

2. Conference Papers

3. Book Chapters

4. Registers

5. Conference Proceedings

6. Conference Abstracts

7. Invited Lectures

Team

Esther Bron, PhD

Bo Li

Wenjie Kang

Myrthe van Haaften

Jing Yu

Alexander Harms

Claudia Chinea Hammecher

Interested in joining?

Former team members