Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning - Wang, Cong; Xie, Shuaining; Li, Kang; Wang, Chongyang; Liu, Xudong; Zhao, Liang; Tsai, Tsung-Yuan (Engineering (Beijing, China), 2021)
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.