October 30, 2017

Two PhD positions on tracking of nerve fibres

Tracking of nerve fibres in brain tumour patients


The TRABIT project

The “Translational Brain Imaging Training Network” (TRABIT) research project aims to bring innovative quantitative neuro image computing methods into the clinic, enabling improved healthcare delivery to patients with brain disease. The research in TRABIT will be performed within a network of 10 R&D institutes spread over Europe, which will host a total of 15 Early Stage Researchers (ERSs), supported by the highly prestigious and competitive Marie Curie Innovative Training Networks (ITN) fellowship program (

Two of these ESRs will be employed in the Netherlands, one by Philips Healthcare and one by the Eindhoven University of Technology (TU/e). Together they will work on the topic of nerve fibre tracking in diffusion-weighted MR brain images, to improve the planning and execution of treatment of brain tumours.  The ESR at TU/e will focus on the development of innovative methods for accurate and robust tracking of nerve fibers in the brain around brain tumors and determining the uncertainty of those.  The ESR at Philips will work on the integration of these algorithms into an easy-to-use, accurate and robust clinical software prototype application, including the clinical validation of this software application.

Goals of the TU/e ESR:

·        Developing and validating accurate, efficient and robust algorithms to track brain nerve fibers in diffusion MRI from patient with pathology.

·        Visualizing these fibers, including their tracking uncertainty.

·        Validating the developed algorithms using a selected set of relevant MR image data (phantoms, healthy volunteers, patients).

·        Publication of results at scientific conferences and in peer-reviewed scientific journals.

·        Defend a PhD thesis at TU/e.


Goals of the Philips ESR:

·        Thorough analysis of the clinical needs regarding neuro fiber tracking in patients with brain tumors.

·        Translation of the clinical needs to functional requirements of the prototype application that will be developed.

·        Design of the architecture and workflow of the prototype implementation, using as much as possible available components of the Philips MR research and product software platforms, and incorporating algorithms developed by ESR9 at TU/e.

·        Implementation, verification and validation of the prototype software application, first in-house, later in a realistic clinical environment.

·        Publication of results at scientific conferences and in peer-reviewed scientific journals.

·        Option to defend a PhD thesis at TU/e.

Supervision and training

The local project leaders are prof. Marcel Breeuwer (Philips Healthcare+TU/e) and prof. Josien Pluim (TU/e), with co-supervision by Dr Alexander Leemans (UMC Utrecht) and dr. Maarten Versluis (Philips Healthcare).

You will be primarily based at either Philips or TU/e, but meet regularly. The ESR at Philips will join the research meetings of the Medical Image Analysis group at TU/e. For this Marie Curie ITN project, you will also work for extended secondments (short externships) at the University Medical Center Utrecht, The Netherlands ( and the Fraunhofer MEVIS Institute, Germany (

The TRABIT project will provide high quality network-wide scientific training courses and complementary skills training.



To apply to one of these positions:

·        ESR at TU/e

·        ESR at Philips Healthcare


More information

For more information about these positions, contact prof. Marcel Breeuwer (Philips Healthcare, or prof. Josien Pluim (TU/e,



·        N. Sepasian et al.,  Multivalued geodesic ray-tracing for computing brain connections using diffusion tensor imaging, SIAM Journal of Imaging Science, 2012

·        N. Sepasian et al., Modified geodesic ray-tracing for diffusion tensor imaging, ISBI 2016

·        C. Tax et al., Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery, PLoS ONE, 2014

·        C. Tax, Less confusion in Diffusion MRI, PhD thesis, 2016



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