Ir. Annegreet van Opbroek

Annegreet van Opbroek

Office: Na 2614
Phone: +31-10-70444118

Curriculum Vitae

Anna Gretha (Annegreet) van Opbroek was born in 1986 in Woubrugge, the Netherlands. In 2004 she finished secondary school in Tilburg with the predicate `Cum Laude’ and started a Bachelor in Applied Mathematics at the Delft University of Technology. During her Bachelor she specialized in probability, statistics, and stochastic research and broadened her knowledge with a minor in Applied Physics. Afterwards, Annegreet did a Master in Biomedical Engineering with specialization in Medical Imaging. Following her enthousiasm for probability&statistics, she soon became intrigued with machine learning. She conducted her masters thesis at the Biomedical Imaging Group Rotterdam (BIGR) at the Erasmus Medical Centre in Rotterdam, the Netherlands, on the application of transductive transfer-learning techniques in biomedical image segmentation.

Following her master thesis, Annegreet started a PhD at the BIGR group in October 2011 on the application of transfer learning for biomedical image segmentation. She was supervised by Wiro Niessen (promotor) and Marleen de Bruijne (co-promotor). She investigated the applicability of transfer-learning techniques to account for differences between training and test images, such as differences in used scanners, scanning protocols, and patient characteristics. She also herself developed several new transfer-learning techniques for medical image segmentation. The application of her project mainly lies in MRI brain segmentation.

As of September 2015, Annegreet works as a post-doctoral researcher at the BIGR group on a multi-center project on the development of IT infrastructure for large imaging studies. She coordinates software developers at the different centers and also develops and robustifies neuro-image-segmentation techniques for large multi-center datasets.

Research lines

Model-based Medical Image Analysis

Neuro Image Analysis


Transfer Learning in Biomedical Image Analysis


Journal Papers

A. van Opbroek, M. Vernooij, M. Ikram, and M. de Bruijne. Weighting training images by maximizing distribution similarity for supervised segmentation across scanners. Medical Image Analysis, 24(1):245-254, 2015.

A. Mendrik, K. Vincken, H. Kuijf, M. Breeuwer, W. Bouvy, J. de Bresser, A. Alansary, M. de Bruijne, A. Carass, A. El-Baz, A. Jog, R. Katyal, A. Khan, F. van der Lijn, Q. Mahmood, R. Mukherjee, A. van Opbroek, S. Paneri, S. Pereira, M. Persson, M. Rajchl, D. Sarikaya, "O. Smedby, C. Silva, H. Vrooman, S. Vyas, C. Wang, L. Zhao, G. Biessels, and M. Viergever. MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans. Computational Intelligence and Neuroscience, 2015:1–16, 2015.

A. van Engelen, A. van Dijk, M. Truijman, R. van ‘t Klooster, A. van Opbroek, A. van der Lugt, W. Niessen, M. Kooi, and M. de Bruijne. Multi-center MRI carotid plaque component segmentation using feature normalization and transfer learning. Medical Imaging, IEEE Transactions on, 34(6):1294-1305, 2015.

A. van Opbroek, M. Ikram, M. Vernooij, and M. de Bruijne. Transfer learning improves supervised image segmentation across imaging protocols. Medical Imaging, IEEE Transactions on, 34(5):1018-1030, 2015.

M. Dekking, D. Kong, and A. van Opbroek. Plumes in kinetic transport: how the simple random walk can be too simple. Stochastic Models, 28(4): 635-648, 2012.

Conference Papers

V. Cheplygina, A. van Opbroek, M. Ikram, M. Vernooij, and M. de Bruijne. Asymmetric similarity-weighted ensembles for image segmentation. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pages 273-277. IEEE, 2016.

A. van Opbroek, H. Achterberg, and M. de Bruijne. Feature-space transformation improves supervised segmentation across scanners. In Machine Learning meets Medical Imaging, pages 85-93. Springer, 2015.

A. van Opbroek, M. Ikram, M. Vernooij, and M. de Bruijne. A transfer-learning approach to image segmentation across scanners by maximizing distribution similarity. In Machine Learning in Medical Imaging, pages 49-56. Springer, 2013.

A. van Opbroek, F. van der Lijn, and M. de Bruijne. Automated brain-tissue segmentation by multi-feature SVM classification. Grand Challenge on MR Brain Image Segmentation workshop – MRBRainS13, 2013.

A. van Opbroek, M. Ikram, M. Vernooij, and M. de Bruijne. Supervised image segmentation across scanner protocols: A transfer learning approach. In Machine Learning in Medical Imaging, pages 160-167. Springer, 2012.

Conference Abstracts

H. Achterberg, A. van Opbroek, M. Koek, D. Bos, M. Vernooij, M. Ikram, H. Hulshoff Pol, W. Niessen, and A. van der Lugt. Quantitative imaging biomarkers for biobanking. Global Biobank Week, 2017.

T. Kroes, H. Achterberg, B. van Lew, M. Koek, A. van Opbroek, A. Versteeg, and B. Lelieveldt. Pipeline inspection and monitoring. Global Biobank Week, 2017.

M. Koek, H. Achterberg, A. van Opbroek, A. Versteeg, D. Bos, B. van Lew, T. Kroes, M. Zwiers, Y. Caspi, H. Hulshoff Pol, A. van der Lugt, and W. Niessen. Imaging biomarker infrastructure. Global Biobank Week, 2017.

L. Tap, A. van Opbroek, W. Niessen, M. Smits, and F. Mattace-Raso. Vascular aging is associated with the severity of cerebral white matter lesion load. Artery Research 20: 100-101, 2017.