Construction of a Statistical Surgical Plan Atlas for Automated 3D Planning of Femoral Component in Total Hip Arthroplasty

  • Authors:

  • Masahiko Nakamoto;Itaru Otomaru;Masaki Takao;Nobuhiko Sugano;Yoshiyuki Kagiyama;Hideki Yoshikawa;Yukio Tada;Yoshinobu Sato

  • Affiliations:

  • Division of Image Analysis, Graduate School of Medicine, Osaka University, Japan;Graduate School of Engineering, Kobe University, Japan;Department of Orthopaedics, Graduate School of Medicine, Osaka University, Japan;Department of Orthopaedics, Graduate School of Medicine, Osaka University, Japan;The Center for Advanced Medical Engineering and Informatics, Osaka University, Japan;Department of Orthopaedics, Graduate School of Medicine, Osaka University, Japan;Graduate School of Engineering, Kobe University, Japan;Division of Image Analysis, Graduate School of Medicine, Osaka University, Japan

  • Venue:
  • MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
  • Year:
  • 2008

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Abstract

The problem of automating preoperative planning of the femoral component (stem) for total hip arthroplasty (THA) is addressed. In our previous method, time-consuming trial-and-error processes were involved in parameter tuning of the objective function. This problem prevents application in different stem systems. To overcome this problem, a statistical surgical plan atlas (SSPA) is constructed from training datasets of stem planning. The SSPA represents the average and variance of the distance distribution on the stem surface to the femoral canal surface. That is, it encodes the distribution of the degree of contact preferred by the surgeon. Automated planning is performed by minimizing the squared difference between distributions of the SSPA and planning solution. The proposed method involves no parameter tuning to define the objective function that evaluates differences from the planning the surgeon prefers. Experimental evaluations showed that the proposed method renders parameter tuning unnecessary while it still provides comparable accuracy to the previous method.