18 citations · 34 across the 5 of their papers we have counts for
7 papers
Memory-efficient GAN-based Domain Translation of High Resolution 3D Medical Images
Hristina Uzunova, Jan Ehrhardt, Heinz Handels
Generative adversarial networks (GANs) are currently rarely applied on 3D medical images of large size, due to their immense computational demand. The present work proposes a multi…
Robust GPU-based Virtual Reality Simulation of Radio Frequency Ablations for Various Needle Geometries and Locations
Niclas Kath, Heinz Handels, Andre Mastmeyer
Purpose: Radio-frequency ablations play an important role in the therapy of malignant liver lesions. The navigation of a needle to the lesion poses a challenge for both the trainee…
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images
Hristina Uzunova, Jan Ehrhardt, Fabian Jacob +2
Currently generative adversarial networks (GANs) are rarely applied to medical images of large sizes, especially 3D volumes, due to their large computational demand. We propose a n…
Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
Jan Rühaak, Thomas Polzin, Stefan Heldmann +4
We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a d…
Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures
Andre Mastmeyer, Matthias Wilms, Heinz Handels
Virtual reality (VR) training simulators of liver needle insertion in the hepatic area of breathing virtual patients currently need 4D data acquisitions as a prerequisite. Here, fi…
Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures
Andre Mastmeyer, Matthias Wilms, Heinz Handels
Current virtual reality (VR) training simulators of liver punctures often rely on static 3D patient data and use an unrealistic (sinusoidal) periodic animation of the respiratory m…