CT-based automated planning of acetabular cup for total hip arthroplasty (THA) based on hybrid use of two statistical atlases
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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.
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.
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).
We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon’s preference during cup planning.
KeywordsComputer-assisted surgery Surgeon’s expertise Multi-object statistical shape model Residual bone thickness Implant surgery Surgical planning
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.
- 12.Nikou C, Jaramaz B, DiGioia A, Blackwell M, Romesberg M, Green M (1999) POP: preoperative planning and simulation software for total hip replacement surgery. Proc MICCAI 1679:868–875Google Scholar
- 16.Kagiyama Y, Nakamoto M, Takao M, Sato Y, Sugano N, Yoshikawa H, Tamura S (2004) Automated preoperative 3D planning of acetabular cup positioning and size selection in total hip arthroplasty using CT data. Proc CAOS 312–313Google Scholar
- 17.Kagiyama Y, Sugano N, Takao M, Nakamoto M, Sato Y, Yoshikawa H, Akazawa K, Tada Y (2008) Automated preoperative planning procedure for acetabular cup based on 3D pelvic bone structure in total hip replacement. Jpn Soc Med Biol Eng 46(4):437–450 (in Japanese)Google Scholar
- 18.Otomaru I, Kobayashi K, Okada T, Nakamoto M, Takao M, Sugano N, Tada Y, Sato Y (2009) CT-based automated preoperative planning of acetabular cup size and position using pelvis-cup integrated statistical shape model. Proc CAOS 185–188Google Scholar
- 22.Otomaru I, Kobayashi K, Okada T, Nakamoto M, Kagiyama Y, Takao M, Sugano N, Tada Y, Sato Y (2009) Expertise modeling for automated planning of acetabular cup in total hip arthroplasty using combined bone and implant statistical atlases. Proc MICCAI 5761:532–539Google Scholar
- 23.Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2013) Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. Proc MICCAI 8150:190–197Google Scholar
- 25.Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2012) Automated localization of pelvic anatomical coordinate system from 3D CT Data of the hip using statistical atlas. Med Imag Technol 30(1):43–52 (in Japanese)Google Scholar
- 27.Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu DS, Rau AM, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Med Imaging 28:1251–1265CrossRefGoogle Scholar
- 28.Kagiyama Y, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2013) Optimization of surgical planning of total hip arthroplasty based on computational anatomy. Proc IEEE EMBC 2980–2983Google Scholar
- 29.Lamecker H, Wenckebach TH, Hege HC (2006) Atlas-based 3D-shape reconstruction from X-ray images. Proc ICPR 371–374Google Scholar
- 30.Balestra S, Schumann S, Heverhagen J, Nolte G, Zheng G (2014) Articulated statistical shape model-based 2D–3D reconstruction of a hip joint. Proc IPCAI 8498:128–137Google Scholar