Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods.

Journal: The international journal of medical robotics + computer assisted surgery : MRCAS
PMID: 39497376

Abstract

BACKGROUND: Automatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings.

Authors

  • Jiaqi Han
    Department of Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Institute of Medical Robotics and Intelligent Systems, Tianjin University, Tianjin, China.
  • Xinlong Ma
    Tianjin Hospital of Tianjin University (Tianjin Hospital), Tianjin, 300211, People's Republic of China. tjyygystg@163.com.
  • Yiou Lyu
    Department of Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Institute of Medical Robotics and Intelligent Systems, Tianjin University, Tianjin, China.
  • Haohao Bai
    Department of Orthopaedics Institute, Tianjin Hospital, Tianjin University, Tianjin, China.

Keywords

External Resources

Popular Topics
Recent Journals