12 citations · 51 across the 18 of their papers we have counts for
20 papers
ProPLIKS: Probablistic 3D human body pose estimation
Karthik Shetty, Annette Birkhold, Bernhard Egger +4
We present a novel approach for 3D human pose estimation by employing probabilistic modeling. This approach leverages the advantages of normalizing flows in non-Euclidean geometrie…
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
Noah Maul, Annette Birkhold, Fabian Wagner +5
Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) rec…
BOSS: Bones, Organs and Skin Shape Model
Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan +4
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 trackin…
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Noah Maul, Katharina Zinn, Fabian Wagner +6
Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurat…
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan +4
We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for reconstruction of a 3D mesh of the human body from a single 2D image. Current techniques directly regress the shape,…
Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering
Karthik Shetty, Annette Birkhold, Norbert Strobel +4
Many minimally invasive interventional procedures still rely on 2D fluoroscopic imaging. Generating a patient-specific 3D model from these X-ray projection data would allow to impr…