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

Cong Wang, Shuaining Xie, Kang Li, Chongyang Wang, Xudong Liu, Liang Zhao, Tsung Yuan Tsai

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)881-888
Number of pages8
JournalEngineering
Volume7
Issue number6
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Materials Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Engineering(all)

Keywords

  • 2D–3D registration
  • Domain adaption
  • Machine learning
  • Point correspondence

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