Image Guided Interventions
Group Leader: Alena-Kathrin Schnurr
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: Alena-Kathrin Schnurr, Christian Tönnes
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.
Persons involved: Dominik Bauer, Tom Russ, Alena-Kathrin Schnurr
Registration of Abdominal Organs in Medical Images
We investigate (multimodal and interdimensional) volume registration techniques.
Persons involved: Gordian Kabelitz, Barbara Waldkirch (associated)
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, Tom Russ
- Dr.rer.nat. Khanlian Chung
- Alena-Kathrin Schnurr, M.Sc.
- Gordian Kabelitz, M.Sc.
- Tom Russ, M.Sc.
- Dominik Bauer, M.Sc.
- Christian Tönnes, M.Sc. ☕
- Barbara Waldkirch , M.Sc. (associated)
- Lara Behrend
- Aimen Abdelrehim, B.Sc.
- Constantin Ulrich, B.Sc. 🏆
- Marco Schöben 🏆 🏆
- Jan Schlenker
- I Sun
- T. Russ, S. Goerttler, A.-K. Schnurr, D. Bauer, S. Hatamikia, L. Schad, F. Zöllner and K. Chung; Synthesis of CT Images from Digital Body Phantoms Using CycleGANs, International Journal of Computer Assisted Radiology and Surgery, accepted for publication
- Schnurr, K. Chung, T. Russ, L. Schad and F. Zöllner; Simulation-Based Deep Artifact Correction with Convolutional Neural Networks for Limited Angle Artifacts, Z Med Phys, 29 (2), pp.150-161 (2019)
- K. Chung, L. Schad and F. Zöllner; Tomosynthesis implementation with adaptive online calibration on clinical C-arm systems, Int J Comput Assist Radiol Surg, 13 (10), pp.1481-1495 (2018)
|Contact: Dr. Frank Zöllner||last modified: 13.01.2020|