Prof. Dr. Ing.



2003 – 2007  Research Scientist, Computer Graphics Group, University of Erlangen-Nuremberg, Germany, Prof. G. Greiner
2008 – 2009 Research Scientist, Siemens Molecular Imaging, Oxford, UK
2009 – 2013 Assistant Professor (W1) for Visual Computing, University of Konstanz, Germany
2013 – present         Director of the Institute of Imaging & Computer Vision (LfB), RWTH University, Germany


Academic Qualification and Education

2003         Diploma in Computer Science, University of Erlangen-Nuremberg, Germany
2007 Doctor of engineering, University of Erlangen-Nuremberg, Germany
2013 Full Professor at RWTH Aachen University


Teaching Experience

  • Digital Image Processing I (Winter Semester 2013-2019), M.Sc. Level.
  • Digital Image Processing II (Summer Semester 2014-2019), M.Sc. Level.
  • Biomedical Imaging (Winter Semester 2013-2019), M.Sc. Level.
  • Mathematical Methods for Electrical Engineering (Winter Semester 2013-2019), B.Sc. Level.
  • 3D Computer Graphics and Object Modeling (Winter Semester 2012/13), M.Sc. Level.
  • Introduction to Computer Graphics (Winter Semester 2010/11), M.Sc. Level.
  • Scientific Visualization (Summer Semester 2010, Winter Semester 2011/12), M.Sc. Level.
  • Analysis and Visualization of Medical Image Data (Winter Semester 2010/11), M.Sc. Level.

Honors, Awards, Scholarships and Other Appointments (Selection)

  • Honorary Professor at Amity School of Engineering & Technology, Uttar Pradesh, India (2018)
  • Scientific Advisory Committee of the Heidelberg Image Processing Forum (since 2018)
  • Speaker of the GI Special Interest Group “Visual Computing in Biology and Medicine” (2012-2014, 2015-2017)
  • Organizer of the Karl-Heinz-Höhne Award 2012-2016
  • Jury Member for the Friedrich-Wilhelm-Award, RWTH Aachen University (since 2014)
  • Member of the Exploratory Research Space (ERS) Selection Committee, RWTH Aachen University (since 2017)
  • Reviewer for different IEEE and other international scientific journals
  • Member of Program Committees of different national and international conferences

Research Topics

Image Analysis, Computer Vision, Machine Learning, Multidimensional Signal Processing, and Image Data Visualization for medical, biological and industrial image data.

Key Publications

  1. Gadermayr, M., Gupta, L., Appel, V., Boor, P., Klinkhammer, B.M., Merhof, D., “Generative Adversarial Networks for Facilitating Stain-Independent Supervised & Unsupervised Segmentation: A Study on Kidney Histology” IEEE Transactions on Medical Imaging (TMI) (2019) [epub ahead of print, doi 10.1109/TMI.2019.2899364]
  2. Gadermayr, M., Appel, V., Klinkhammer, B.M., Boor, P., Merhof, D., “Which Way Round? A Study on the Performance of Stain-Translation for Segmenting Arbitrarily Dyed Histological Images”, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2018), pp. 165-173
  3. Stiebel, T., Koppers, S., Seltsam, P., Merhof, D., “Reconstructing Spectral Images from RGB-Images using a Convolutional Neural Network”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2018), pp. 948-953
  4. Koppers, S., Friedrichs, M., Merhof, D., “Qualitative Comparison of Reconstruction Algorithms for Diffusion Imaging” Reconstruction of Diffusion Anisotropies using 3D Deep Convolutional Neuronal Networks in Diffusion Imaging, Modeling, Analysis and Visualization of Anisotropy, Mathematics and Visualization, Springer International Publishing (2017), pp. 393-404, ISBN 978-3-86247-577-3.
  5. Unger, J., Mansour, M., Kopazka, M., Gronloh, N., Spehr, M., Merhof, D., “An Unsupervised Learning Approach for Tracking Mice in an Enclosed Area” BMC Bioinformatics 18(272) (2017), pp. 1-14, doi: 10.1186/s12859-017-1681-1
  6. Bug, D., Schneider, S., Grote, A., Oswald, E., Feuerhake, F., Schüler, J., Merhof, D.,“Context-based Normalization of Histological Stains using Deep Convolutional Features” MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA), Lecture Notes in Computer Science, vol 10553 (2017), pp. 135-142
  7. Schneider, D., Gloy, Y.-S., Merhof, D., “Vision based on-loom measurement of yarn densities in woven fabrics” IEEE Transactions on Instrumentation and Measurement (TIM) 64, 4 (2014), pp. 1063-1074.
  8. Merhof, D., Markiewicz, P.J., Platsch, G., Declerck, J., Weih, M., Kornhuber, J., Kuwert, T., Matthews, J.C., Herholz, K. “Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT datasets of Alzheimer's patients and asymptomatic controls” Journal of Cerebral Blood Flow & Metabolism (Nature Publishing Group) 31, 1 (2011), pp. 371-383.
  9. Bishop, C.A., Jenkinson, M., Andersson, J., Declerck, J., Merhof, D., “Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): Method and Validation on Clinical Data” NeuroImage 55, 3 (2011), pp. 1009-1019.
  10. Merhof, D., Soza, G., Stadlbauer, A., Greiner, G., and Nimsky, C., “Correction of susceptibility artifacts in diffusion tensor data using non-rigid registration” Medical Image Analysis 11, 6 (2007), pp. 588-603.