1 citations · 1 across the 7 of their papers we have counts for
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From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform
Benjamin El-Zein, Dominik Eckert, Andreas Fieselmann +4
Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an e…
A Realistic Collimated X-Ray Image Simulation Pipeline
Benjamin El-Zein, Dominik Eckert, Thomas Weber +4
Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presen…
An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter
Dominik Eckert, Ludwig Ritschl, Christopher Syben +5
Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propo…
StyleX: A Trainable Metric for X-ray Style Distances
Dominik Eckert, Christopher Syben, Christian Hümmer +3
The progression of X-ray technology introduces diverse image styles that need to be adapted to the preferences of radiologists. To support this task, we introduce a novel deep lear…
Metal-conscious Embedding for CBCT Projection Inpainting
Fuxin Fan, Yangkong Wang, Ludwig Ritschl +6
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. I…
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
Sebastian Guendel, Sasa Grbic, Bogdan Georgescu +4
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve…