Amsterdam UMC
Steffen Bruns Amsterdam UMC

PhD Candidate
Phone: +31 20 56 65206
LinkedIn; Google Scholar


Steffen studied Medical Engineering Sciences at the University of Lübeck, Germany. Within this program, he got the chance to be an intern at the University of California, Berkeley. He joined the Berkeley Imaging Systems Laboratory where he worked on comparing the sensitivity of MRI to Magnetic Particle Imaging. For his Master thesis, he completed a project on the accelerated reconstruction for small-animal pinhole PET at MILabs in Utrecht, Netherlands.

In October 2017, Steffen started his PhD at the Image Sciences Institute of the UMC Utrecht and joined the Quantitative Medical Image Analysis Group. He is part of the program
Deep learning in medical image analysis (DLMedIA) where he is currently working on the development of novel deep learning algorithms for the automatic analysis of high-dimensional medical images, mainly spectral CT.


S. Bruns, J.M. Wolterink, R.A.P. Takx, R.W. van Hamersvelt, D. Suchá, M.A. Viergever, T. Leiner, I. Išgum

Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT Journal Article

Medical Physics (in press), 2020.

Abstract | Links | BibTeX


S. Bruns, J.M. Wolterink, T.P.W. van den Boogert, J.P. Henriques, J. Baan, R.N. Planken, I. Išgum

Automatic whole-heart segmentation in 4D TAVI treatment planning CT Inproceedings

SPIE Medical Imaging (in press), 2020.

Abstract | BibTeX


S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, M. Zreik, T. Leiner, I. Išgum

Improving myocardium segmentation in cardiac CT angiography using spectral information Inproceedings

SPIE Medical Imaging, 2019.

Abstract | Links | BibTeX


S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, T. Leiner, I. Išgum

CNN-based segmentation of the cardiac chambers and great vessels in non-contrast-enhanced cardiac CT Conference

Medical Imaging with Deep Learning. MIDL London, 2019.

Abstract | Links | BibTeX