Search Results for author:
Found 10 papers, 5 papers with code
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments
Here, we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection.
The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective
Image-based navigation is widely considered the next frontier of minimally invasive surgery.
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i. e. verification of screw placement.
Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration
We propose a novel Projective Spatial Transformer module that generalizes spatial transformers to projective geometry, thus enabling differentiable volume rendering.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
By using these annotations as training data for neural networks, state of the art performance in fluoroscopic segmentation and landmark localization was achieved.
Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction.
Localizing dexterous surgical tools in X-ray for image-based navigation
This manuscript describes a first step towards leveraging semantic information of the imaged object to initialize 2D/3D registration within the capture range of image-based registration by performing concurrent segmentation and localization of dexterous surgical tools in X-ray images.
Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks
Our method results in a mean absolute error of 0. 814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.
