Auto-differentiable digitally reconstructed radiographs in PyTorch

Library and executables for modeling and registration applications in medical image analysis. Particular emphasis on intraoperative fluoroscopic (X-ray) navigation via 2D/3D registration.

[CVPR 2024] Intraoperative 2D/3D registration via differentiable X-ray rendering

Code and data for the "annotation" component of the IPCAI 2020 paper: "Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration." https://arxiv.org/abs/1911.07042 or https://doi.org/10.1007/s11548-020-02162-7

Code for the registration component of the IPCAI 2020 paper: "Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration." https://arxiv.org/abs/1911.07042 or https://doi.org/10.1007/s11548-020-02162-7

Code for ICASSP 2024 paper"Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration"

Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences

[WACV2022] "Shape-Coded ArUco: Fiducial Marker for Bridging 2D and 3D Modalities".

MOVED HERE: https://github.com/rg2/xreg