Adriënne has a bachelor degree in computer science and a master degree (cum laude) and PhD degree in biomedical image science.

Her PhD research at the image sciences institute (ISI) in the UMC Utrecht focused on image processing to reduce the X-ray radiation dose in computed tomography (CT) scans while maintaining image quality. She developed three noise reduction methods (one for 3D and two for 4D data) to improve the image quality of CT scans acquired with low radiation dose. As well as a method to derive vascular information from cerebral 4D CT perfusion (CTP) scans that has the potential to replace the additional CT angiography (CTA) scan.

As a post-doc at the biomedical image analysis group (BIGR) in the Erasmus MC (Rotterdam) she focused on noise reduction in 3D XperCT scans acquired with the C-arm CBCT system and compressed sensing, after which she returned to the ISI for her post-doc research on quantitative analysis of MR brain scans for cerebrovascular disease management.

During her post-doc research she developed an interest in grand challenges (, which are open scientific competitions that use evaluation data and metrics to rank the performance of algorithms with respect to an objective. She has organized the MICCAI ( grand challenge on MR brain image segmentation (MRBrainS) workshop in Nagoya (Japan) and has set-up and maintains the open MRBrainS challenge evaluation framework ( At IEEE ISBI 2015 ( in New York, she organized the challenge workshop on neonatal and adult MR brain image segmentation ( She is chair of the challenge workshops at IEEE ISBI ’16 and ’17 and co-organized the tutorial on designing benchmarks and challenges for measuring algorithm performance in biomedical image analysis at IEEE ISBI ’16. At the NFBIA Summer School 2015, she gave a workshop on designing challenges in biomedical image analysis.

She was initiator and organizer of the ImagO colloquium series on medical imaging for the PhD programme of the graduate school of life sciences in the UMC Utrecht and was in the program committee of the international workshop on machine learning in medical imaging (MLMI) ’15 and ’16 at MICCAI.

In October 2016, Adriënne joined the Netherlands eScience Center as an eScience coordinator.

Her research is currently focussed on representation learning and designing a theoretical framework for grand challenges in biomedical image analysis.

Key skills

  • Computer vision
  • Image processing
  • Medical image analysis
  • Algorithm validation
  • Grand challenges

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