259 citations · 292 across the 7 of their papers we have counts for
7 papers
Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery
Maximilian Weber, Daniel Wild, Jens Kleesiek +2
Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR)…
Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
Antonio Pepe, Richard Schussnig, Jianning Li +3
Shape reconstruction from imaging volumes is a recurring need in medical image analysis. Common workflows start with a segmentation step, followed by careful post-processing and,fi…
DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality
Christina Gsaxner, Shohei Mori, Dieter Schmalstieg +4
Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpainting to halluci…
Medical Deep Learning -- A systematic Meta-Review
Jan Egger, Christina Gsaxner, Antonio Pepe +5
Deep learning (DL) has remarkably impacted several different scientific disciplines over the last few years. E.g., in image processing and analysis, DL algorithms were able to outp…
A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge
Jianning Li, Antonio Pepe, Christina Gsaxner +2
In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge, which, as suggested by the organize…
An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design
Jianning Li, Antonio Pepe, Christina Gsaxner +1
We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfens…
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
Christina Gsaxner, Peter M. Roth, Jürgen Wallner +1
We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an i…