公开11万张双平面膝关节假体X光图，助力医学影像深度学习训练
Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging
- 构建双平面荧光成像数据集，含10种假体组合的1600次运动试验
- 提供约11万张图像，200张带人工标注姿态用于算法基准测试
- 适合医疗影像、计算机视觉领域研究者用于注册与重建算法开发
Veriserum 是一个开源数据集，旨在支持双平面荧光成像分析中深度学习配准算法的训练。数据集包含约11万张来自10种膝关节假体组合（2种股骨和5种胫骨假体）的X射线图像，采集自1600次试验，涵盖平地行走、上坡下行等日常活动姿态。每张图像均配有自动注册的真值姿态，另有200张图像附有手动标注姿态以供基准评估。数据集包含双平面图像与校准工具，可支持2D/3D图像配准、图像分割、X射线畸变校正及三维重建等应用。该数据集免费开放，可通过 https://movement.ethz.ch/data-repository/veriserum.html 获取，存储于ETH Zürich研究数据库：https://doi.org/10.3929/ethz-b-000701146。
原文摘要 · Abstract (English)
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.
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