3 citations · 4 across the 2 of their papers we have counts for
4 papers
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi +4
Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compe…
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi +4
Involuntary motion during weight-bearing cone-beam computed tomography (CT) scans of the knee causes artifacts in the reconstructed volumes making them unusable for clinical diagno…
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Jennifer Maier, Luis Carlos Rivera Monroy, Christopher Syben +7
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartila…
Fooling the Crowd with Deep Learning-based Methods
Christian Marzahl, Marc Aubreville, Christof A. Bertram +6
Modern, state-of-the-art deep learning approaches yield human like performance in numerous object detection and classification tasks. The foundation for their success is the availa…