Medical Image Computing (MIC)

Medical Image Computing (MIC)

Medical Image Computing (MIC) left to right: Jonas Cordes, Christoph Kolb, Josiah Blöcher, Thomas Van Bruggen*, Jan Hering, Tobias Norajitra, Klaus H. Maier-Hein, Christian Weber, Romy Henze, Michael Götz, Julia , Caspar Jonas Goch, Moritz Petry; Tawfik Moher Alsady **, Peter Neher **, Bram Stieltjes ** (*alumni **missing)
© dkfz.de

The success of therapies depends on medical decisions that are tailored to the individual patient. In this context, a medical imaging-based characterization of structural, functional, and anatomical tissue properties is essential. Considering the increasing complexity of medical imaging technologies, it becomes more and more difficult for physicians to extract and integrate relevant information from multiple scales, time-points, and imaging modalities when it comes to the diagnosis of complex and individual diseases. The “Computational Disease Analysis” group with Dr. Klaus Maier-Hein (né Fritzsche) as PI was initiated in April 2010 to meet these challenges. The group became an independent research group called “Medical Image Computing” in June 2014.

The group is developing computational and mathematical methods for an extraction and interpretation of clinically relevant parameters from medical images. Our development comprises techniques from network analysis, graphical modeling, statistical shape and appearance modelling, and machine learning for multi-parametric domain integration and high-level decision making. The group especially focuses on the neuroimaging-based analysis of the human brain’s network architecture and its dynamic change in different psychiatric and oncologic scenarios.

Taking part in the collaborative research centre “Cognition-guided surgery” (SFB 125) with Dr. Maier-Hein as project leader for multi-parametric data- and image analysis, the group strives for a methodological translation to the fields of abdominal and thoracic imaging.

Selected publications

Bach, Michael, Frederik B. Laun, Alexander Leemans, Chantal M. W. Tax, Geert J. Biessels, Bram Stieltjes, and Klaus H. Maier-Hein. “Methodological Considerations on Tract-Based Spatial Statistics (TBSS).” NeuroImage 100 (October 15, 2014): 358–69. doi:10.1016/j.neuroimage.2014.06.021.

Goetz, Michael, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Koethe, Jens Kleesiek, Bram Stieltjes, and Klaus Maier-Hein. “DALSA: Domain Adaptation for Supervised Learning from Sparsely Annotated MR Images.” IEEE Transactions on Medical Imaging, 2015, 1–1. doi:10.1109/TMI.2015.2463078.

Hering, Jan, Ivo Wolf, and Maier-Hein, Klaus H. “Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.” IEEE Transactions on Medical Imaging, 2016

Maier-Hein, Klaus H., Carl-Fredrik Westin, Martha E. Shenton, Michael W. Weiner, Ashish Raj, Philipp Thomann, Ron Kikinis, Bram Stieltjes, and Ofer Pasternak. “Widespread White Matter Degeneration Preceding the Onset of Dementia.” Alzheimer’s & Dementia 11, no. 5 (May 2015): 485–93.e2. doi:10.1016/j.jalz.2014.04.518.

 

Neher, Peter F., Maxime Descoteaux, Jean-Christophe Houde, Bram Stieltjes, and Klaus H. Maier-Hein. "Strengths and Weaknesses of State of the Art Fiber Tractography Pipelines - A Comprehensive in-Vivo and Phantom Evaluation Study Using Tractometer." Medical Image Analysis 26, no. 1 (December 2015): 287-305. doi:10.1016/j.media.2015.10.011

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