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Division of Medical Image Computing

Prof. 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

The Division of Medical Image Computing (MIC) pioneers research in machine learning and information processing, with the particular aim of improving cancer patient care by systematic image data analytics. We structure and quantify imaging information from multiple time-points and imaging technologies, e.g. magnetic resonance imaging or computer tomography, and link it with clinical and biological parameters. As an initiator and co-coordinator of the Helmholtz Imaging Platform (HIP) we pursue cutting-edge developments at the core of computer science, with applications in but also beyond medicine. We are particularly interested in techniques for semantic segmentation and object detection as well as in unsupervised learning and probabilistic modeling.

Methodologic excellence can only be achieved on the basis of a sophisticated research software system and infrastructure, for example to facilitate highly scalable data analysis in a federated setting. Our technological portfolio in this regard builds the foundation of various national and international clinical research networks, such as the National Center for Tumor Diseases (NCT), the German Cancer Consortium (DKTK) and the Cancer Core Europe (CCE). In collaboration with our clinical partners, we work on the direct translation of the latest machine learning advances into relevant clinical applications.

Our vision is to advance the quality of healthcare through methodological advances in artificial intelligence research and their large-scale clinical implementation. We therefore have a particular interest in techniques that improve the applicability of data science in clinical settings, e.g. by providing more interpretable decision-making, by explicitly dealing with data uncertainty, by increasing the generalizability of algorithms or by learning more powerful representations. We further study image computing concepts that combine mathematical modelling approaches with current machine learning techniques. We are dedicated to open science and committed to maintaining several open source projects in order to share our advances with developers and the scientific community and to promote leveraging synergies.

Contact

Prof. Dr. Klaus Maier-Hein
Medical Image Computing (E230)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 223
69120 Heidelberg
Tel: +49 6221 42 2327
Fax: +49 6221 42 2345

Selected Publications

  • Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019 May;20(5):728-740. doi: 10.1016/S1470-2045(19)30098-1. Epub 2019 Apr 2.
  • Isensee F, Jaeger PF, Kohl SAA, Petersen J and Maier-Hein K. nnUNet: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18, 203-2011 (2021); https://doi.org/10.1038/s41592-020-01008-z
  • Wasserthal J, Neher P, Maier-Hein KH: TractSeg - Fast and accurate white matter tract segmentation. Neuroimage 183, 239-253, 2018.
  • Maier-Hein KH, Neher PF, Houde JC, Cote MA, Garyfallidis E, Zhong J, Chamberland M, Yeh FC, Lin YC, Ji Q, Reddick WE, Glass JO, Chen DQ, Feng Y, Gao C, Wu Y, Ma J, Renjie H, Li Q, Westin CF, Deslauriers-Gauthier S, Gonzalez JOO, Paquette M, St-Jean S, Girard G, Rheault F, Sidhu J, Tax CMW, Guo F, Mesri HY, David S, Froeling M, Heemskerk AM, Leemans A, Bore A, Pinsard B, Bedetti C, Desrosiers M, Brambati S, Doyon J, Sarica A, Vasta R, Cerasa A, Quattrone A, Yeatman J, Khan AR, Hodges W, Alexander S, Romascano D, Barakovic M, Auria A, Esteban O, Lemkaddem A, Thiran JP, Cetingul HE, Odry BL, Mailhe B, Nadar MS, Pizzagalli F, Prasad G, Villalon-Reina JE, Galvis J, Thompson PM, Requejo FS, Laguna PL, Lacerda LM, Barrett R, Dell`Acqua F, Catani M, Petit L, Caruyer E, Daducci A, Dyrby TB, Holland-Letz T, Hilgetag CC, Stieltjes B, Descoteaux M: The challenge of mapping the human connectome based on diffusion tractography. Nature Communications 8 (1), 1349, 2017.
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