Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional (2D) to three-dimensional (3D) data with a broad range of capture. However, if there are insufficient data for training, the data-driven approach will fail. We propose a feature-based transfer-learning method to extract features from fluoroscopic images. With three subjects and fewer than 100 pairs of real fluoroscopic images, we achieved a mean registration success rate of up to 40%. The proposed method provides a promising solution, using a learning-based registration method when only a limited number of real fluoroscopic images is available.

深度学习方法为测量体内膝关节运动提供了一种很有前景的方法，从二维 (2D) 到具有广泛捕获范围的三维 (3D) 数据的快速配准。但是，如果没有足够的数据进行训练，数据驱动的方法就会失败。我们提出了一种基于特征的迁移学习方法来从透视图像中提取特征。在三个受试者和不到 100 对真实透视图像的情况下，我们实现了高达 40% 的平均配准成功率。所提出的方法提供了一种有前途的解决方案，当只有有限数量的真实透视图像可用时，使用基于学习的配准方法。