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Image Guided Interventions

Research Profile

Segmentation of Abdominal Organs in Medical Images
We investigate AI-based algorithms for fast and robust segmentation of anatomical structures in CT and MRI. Our main focus is the optimization of the preprocessing pipeline and the application to abdominal images.

Persons involved: Anika Strittmatter, Anish Raj, Christian Tönnes, Christoph Schellenberger

Synthetic CT Images as Training Data for Neural Networks
There is often a lack of annotated medical image data to train deep learning algorithms. Our approach to obtaining training data is to synthesize realistic image data from the XCAT phantom via a CycleGAN network. The XCAT phantom allows the integration of anatomical variability in combination with pixel-precise annotation masks. The synthetic images can be used to train neural networks such as segmentation or registration networks.

Registration of Abdominal Organs in Medical Images

We investigate (multimodal and interdimensional) volume registration techniques.

Persons involved: Anika Strittmatter

Alternative Trajectories for Cone Beam Computed Tomography
We investigate new trajectories for CBCT to improve imaging performance, reduce the associated dose, and avoid collisions with robots, humans, and stationary objects. For this purpose, we have access to a programmable interface of an Artis Zeego robot, which provides us with the unique capability to implement arbitrary source-detector trajectories on a clinical CBCT system.

Persons involved: Christian Tönnes

M²IBID: Bildverarbeitung im M²OLIE Closed-Loop

Group members

Former group members

Student members

  • Lara Behrend
  • Jenny Stehr


  • Dr. sc. hum. Dominik Bauer
  • Dr. rer. nat. Tom Russ
  • Dr. sc. hum. Alena-Kathrin Golla
  • Dr. rer. nat. Khanlian Chung
  • Dr. sc. hum. Gordian Kabelitz
  • Dr. sc. hum. Barbara Waldkirch (associated)

Recent publications

  • D. Bauer, A. Adlung, I. Brumer, A. Golla, T. Russ, E. Oelschlegel, F. Tollens, S. Clausen, P. Aumüller, L. Schad, D. Nörenberg and F. Zöllner.
    An Anthropomorphic Pelvis Phantom for MR-guided Prostate Interventions.
    Magn Reson Med, 2021, , Press .

  • D. Bauer, T. Russ, B. Waldkirch, C. Tönnes, W. Segars, L. Schad, F. Zöllner and A. Golla.
    Generation of annotated multimodal ground truth datasets for abdominal medical image registration.
    Int J Comput Ass Rad, 2021, 16 (8), pp.1277-1285 .

  • A. Golla, C. Tönnes, T. Russ, D. Bauer, M. Froelich, S. Diehl, S. Schoenberg, M. Keese, L. Schad, F. Zöllner and J. Rink.
    Automated Screening for Abdominal Aortic Aneurysm in CT scans under clinical conditions using Deep Learning.
    Diagnostics, 2021, 11 (11), p.2131 .

  • A. Golla, D. Bauer, R. Schmidt, T. Russ, D. Nörenberg, K. Chung, C. Tönnes, L. Schad and F. Zöllner.
    Convolutional Neural Network EnsembleSegmentation with Ratio-based Sampling for theArteries and Veins in Abdominal CT Scans.
    IEEE Trans. Biomed. Eng., 2021, 68 (5), pp.1518-1526 .

  • C. Tönnes, S. Janssen, A. Schnurr, T. Uhrig, K. Chung, L. Schad and F. Zöllner.
    Deterministic Arterial Input Function selection in DCE-MRI for automation of quantitativeperfusion calculation of colorectal cancer.
    Magn. Reson. Imaging, 2021, 75, pp.116-123 .