Search Results for author:
Found 7 papers, 1 papers with code
BOSS: Bones, Organs and Skin Shape Model
Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions.
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences.
Ranked #2 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering
First, a statistical human shape model of the human anatomy and second, a differentiable X-ray renderer.
X-ray Scatter Estimation Using Deep Splines
Algorithmic X-ray scatter compensation is a desirable technique in flat-panel X-ray imaging and cone-beam computed tomography.
Medical Physics Image and Video Processing
Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning
Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose.
Action Learning for 3D Point Cloud Based Organ Segmentation
From this we conclude that our method is robust, and we believe that our method can be successfully applied to many more applications, in particular, in the interventional imaging space.
3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation
Using a combination of both methods, our evaluation on 11 well-contrasted clinical datasets yielded an error of 7. 9+/-6. 3 mm over all frames.
