Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning

• Published in: Engineering (Beijing, China) Vol. 7; no. 6; pp. 881 - 888
• Main Authors: Wang, Cong, Xie, Shuaining, Li, Kang, Wang, Chongyang, Liu, Xudong, Zhao, Liang, Tsai, Tsung-Yuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2021; School of Biomedical Engineering & Med-X Research Institute,Shanghai Jiao Tong University,Shanghai 200030,China; Shanghai Key Laboratory of Orthopaedic Implants & Clinical Translational R&D Center of 3D Printing Technology,Department of Orthopaedic Surgery,Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine; SenseTime Research,Shanghai 200233,China; Engineering Research Center of Digital Medicine and Clinical Translation,Ministry of Education,Shanghai 200030,China%SenseTime Research,Shanghai 200233,China%Department of Orthopaedics,New Jersey Medical School,Rutgers University,Newark,NJ 07103,USA%Department of Orthopaedics,Shanghai Jiao Tong University Affiliated Sixth People's Hospital,Shanghai 200233,China; Elsevier
• Subjects: 2D–3D registration; Domain adaption; Machine learning; Point correspondence; Point correspondence; 2D–3D registration; Machine learning; Domain adaption; 2D-3D registration
• ISSN: 2095-8099
• DOI: 10.1016/j.eng.2020.03.016

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.