no code implementations • 8 Mar 2023 • Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan, Norbert Strobel, Bernhard Egger, Markus Kowarschik, Andreas Maier
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.
1 code implementation • CVPR 2023 • Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan, Norbert Strobel, Markus Kowarschik, Andreas Maier, Bernhard Egger
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)
no code implementations • 14 Oct 2022 • Srikrishna Jaganathan, Maximilian Kukla, Jian Wang, Karthik Shetty, Andreas Maier
Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training.
no code implementations • 21 Jul 2021 • Srikrishna Jaganathan, Jian Wang, Anja Borsdorf, Karthik Shetty, Andreas Maier
A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy.
no code implementations • 4 Feb 2021 • Karthik Shetty, Annette Birkhold, Norbert Strobel, Bernhard Egger, Srikrishna Jaganathan, Markus Kowarschik, Andreas Maier
First, a statistical human shape model of the human anatomy and second, a differentiable X-ray renderer.
no code implementations • 4 Feb 2021 • Srikrishna Jaganathan, Jian Wang, Anja Borsdorf, Andreas Maier
We aim to address this gap by incorporating traditional methods in deep neural networks using known operator learning.