Veriserum
Overview
Veriserum is an open-source dataset designed to support the training of deep learning registration for dual-plane fluoroscopic analysis. It comprises approximately 110,000 X-ray images of 10 knee implant pair combinations (2 femur and 5 tibia implants) captured during 1,600 trials, incorporating poses associated with daily activities such as level gait and ramp descent. Each image is annotated with an automatically registered ground-truth pose, while 200 images include manually registered poses for benchmarking. Key features of Veriserum include dual-plane images and calibration tools. The dataset aims to support the development of applications such as 2D/3D image registration, image segmentation, X-ray distortion cor- rection, and 3D reconstruction. Freely accessible, Veriserum aims to ad- vance computer vision and medical imaging research by providing a re- producible benchmark for algorithm development and evaluation.
The Veriserum dataset used in this study is publicly available via external page https://doi.org/10.3929/ethz-b-000701146.
Key Features
- Dual-Plane Fluoroscopy: Paired images from two perspectives facilitate 3D reconstruction and pose estimation.
- Implant Phantoms: Redesigned Synthetic implant structures allow for detailed testing of algorithms.
- Pre-calibrated Images: The images are standardized in size and intensity, ready for direct application with common deep learning models.
- Open Source: Free for academic and research use, promoting transparency and collaboration.