DKFZ Junior Group Medical Image Computing

PD Dr. Klaus Maier-Hein

Magnetic resonance imaging produces a wealth of information which we combine in personally adapted computational models of living organs. This image shows a graph theory-based model of the human brain that helps us learn, detect, and predict disease patterns.
© dkfz.de

Radiologic images uniquely represent the spatial fingerprints of a progressing disease over time. “Radiomics” coins the emerging endeavor to systematically extract, mine and leverage this rich information in a personalized medicine approach. The Medical Image Computing group establishes and studies comprehensive imaging phenotypes reflecting multiple time-points and modalities that can be directly linked to other information sources such as clinical, biological, genomic or proteomic parameters.

This challenge requires novel developments at the core of computer science as well as close collaboration with research units from radiology, medical physics and oncology to enable successful translation to the clinic. Research topics include automated image understanding for anatomical structure detection and lesion segmentation as well as derivation of quantitative imaging biomarkers. Our special interest is in investigating the use of data-driven paradigms such as deep and weak learning strategies for building robust models and tapping the full potential of the information encoded in the images.

To achieve successful validation and translation of the developed computational techniques, we have established a strong focus and long track record on research software engineering and open source projects. Our technology stack covers interactive exploration as well as high-throughput automated analysis of imaging data.

Contact

PD Dr. Klaus Maier-Hein
Medical Image Computing (E132)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 280
69120 Heidelberg
Tel: +49 6221 42 3545

Selected Publications

  • Goetz M, Weber C, Binczyk F, Polanska J, Tarnawski R, Bobek-Billewicz B, Koethe U, Kleesiek J, Stieltjes B, Maier-Hein KH: DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images. IEEE Transactions on Medical Imaging 35, no. 1, 2016. doi:10.1109/TMI.2015.2463078.
  • Hering J, Wolf I, Maier-Hein KH: Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI. IEEE Transactions on Medical Imaging, April 21, 2016. doi:10.1109/TMI.2016.2557580.
  • Norajitra T, Maier-Hein KH: 3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection. IEEE Transactions on Medical Imaging, August 16, 2016. doi:10.1109/TMI.2016.2600502.
  • Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I: The Medical Imaging Interaction Toolkit: challenges and advances. International Journal of Computer Assisted Radiology and Surgery, 1-14, 2013. doi: 10.1007/s11548-013-0840-8.
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