dr. H.J. (Hugo) Kuijf Assistant Professor

Hugo Kuijf (1986) was born in Woerden and grew up in Linschoten, the Netherlands. He completed his Computer Science Bachelor at the Utrecht University in 2007, with academic minors in Software Technology and Game- and Mediatechnology. During his Master Game and Media Technology, he developed an interest in medical image processing. He took extracurricular courses offered by the Biomedical Image Sciences Master programme and finished his Masters' with an internship at the Netherlands Cancer Institute (NKI-AvL), Amsterdam. End of 2009, Hugo started as a PhD-candidate at the Image Sciences Institute (ISI), UMC Utrecht, and focussed on image processing techniques for brain MR images. After completing his PhD in 2013, Hugo continued as a postdoctoral researcher at the ISI.

His research focuses on innovative image processing techniques for the quantification and assessment of brain MR images. These techniques are applied in the context of brain anatomy and pathology, in particular small vessel disease. Advances in MR imaging and the introduction of 7T MR scanners have increased the amount of data and level of detail that can be acquired. Image analysis solutions have become indispensable to quantify and assess brain MR images.

The next goal is to quantify small vessel disease biomarkers, combining the strengths of both clinical 3T and state-of-the-art 7T MR imaging. Semi-automated techniques for the detection of microbleeds, microinfarcts, small arteries and veins, perivascular spaces, and white matter hyperintensities are developed. Translating knowledge and techniques developed at 7T imaging to clinically available 3T imaging is of high interest and will enable broad utilization.

Currently, Hugo is working on the project "Quantitative analysis of MR brain images for cerebrovascular disease management (brainbox)". He co-organized the MICCAI grand challenge on MR brain image segmentation (MRBrainS) workshop in Nagoya (Japan). He developed freely available software for the detection of the midsagittal plane and suface and lesion-symptom mapping.