CT-based automated planning of acetabular cup for total hip arthroplasty (THA) based on hybrid use of two statistical atlases

  • Yoshiyuki Kagiyama
  • Itaru Otomaru
  • Masaki Takao
  • Nobuhiko Sugano
  • Masahiko Nakamoto
  • Futoshi Yokota
  • Noriyuki Tomiyama
  • Yukio Tada
  • Yoshinobu Sato
Original Article

Abstract

Purpose

This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness.

Methods

From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon’s expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration.

Results

The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritis patients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation).

Conclusion

We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon’s preference during cup planning.

Keywords

Computer-assisted surgery Surgeon’s expertise Multi-object statistical shape model Residual bone thickness Implant surgery Surgical planning 

Notes

Acknowledgments

This work was partly supported by MEXT/JSPS KAKENHI No. 26108004 and No. 25242051 and No. 15K21035 and also supported by AMED-ETH Strategic Japanese-Swiss Cooperative Research Program #J130701469.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

The study has been approved by the Institutional Review Board of Osaka University Hospital where the patient’s medical data used in this work were obtained.

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Copyright information

© CARS 2016

Authors and Affiliations

  • Yoshiyuki Kagiyama
    • 1
  • Itaru Otomaru
    • 2
    • 3
    • 4
  • Masaki Takao
    • 5
  • Nobuhiko Sugano
    • 6
  • Masahiko Nakamoto
    • 3
    • 7
  • Futoshi Yokota
    • 9
  • Noriyuki Tomiyama
    • 3
  • Yukio Tada
    • 8
  • Yoshinobu Sato
    • 9
  1. 1.Graduate Faculty of Interdisciplinary ResearchUniversity of YamanashiKofuJapan
  2. 2.Graduate School of EngineeringKobe UniversityKobeJapan
  3. 3.Department of Radiology, Graduate School of MedicineOsaka UniversitySuitaJapan
  4. 4.Canon Inc.OtaJapan
  5. 5.Department of Orthopaedic Surgery, Graduate School of MedicineOsaka UniversitySuitaJapan
  6. 6.Department of Orthopaedic Medical Engineering, Graduate School of MedicineOsaka UniversitySuitaJapan
  7. 7.EBM Inc.OtaJapan
  8. 8.Graduate School of System InformaticsKobe UniversityKobeJapan
  9. 9.Graduate School of Information ScienceNara Institute of Science and Technology (NAIST)IkomaJapan

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