Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914091160829952 |
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| author | Wang, Jinhao Vogl, Florian Schütz, Pascal Ćuković, Saša Taylor, William R. |
| author_facet | Wang, Jinhao Vogl, Florian Schütz, Pascal Ćuković, Saša Taylor, William R. |
| contents | 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 correction, and 3D reconstruction. Freely accessible, Veriserum aims to advance computer vision and medical imaging research by providing a reproducible benchmark for algorithm development and evaluation. The Veriserum dataset used in this study is publicly available via https://movement.ethz.ch/data-repository/veriserum.html, with the data stored at ETH Zürich Research Collections: https://doi.org/10.3929/ethz-b-000701146. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_05483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging Wang, Jinhao Vogl, Florian Schütz, Pascal Ćuković, Saša Taylor, William R. Computer Vision and Pattern Recognition 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 correction, and 3D reconstruction. Freely accessible, Veriserum aims to advance computer vision and medical imaging research by providing a reproducible benchmark for algorithm development and evaluation. The Veriserum dataset used in this study is publicly available via https://movement.ethz.ch/data-repository/veriserum.html, with the data stored at ETH Zürich Research Collections: https://doi.org/10.3929/ethz-b-000701146. |
| title | Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.05483 |