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’s mission is to translate artificial intelligence (AI) to clinical practice, so future patients can be diagnosed and treated based on knowledge gained from previous patients. Therefore, she develops with her team novel image analysis and prediction methodology for neurodegenerative diseases and strongly collaborates with clinicians and clinical researchers to validate these methods for clinical application.

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.

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

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, 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