The accuracy of statistical shape models in predicting bone shape: a systematic review
Author(s)
Patil, Amogh
Kulkarni, Krishan
Xie, Shuqiao
Bull, Anthony MJ
Jones, Gareth G
Type
Journal Article
Abstract
Background
This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.
Methods
A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.
Results
2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).
Conclusion
Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.
This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.
Methods
A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.
Results
2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).
Conclusion
Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.
Date Issued
2023-06
Date Acceptance
2023-01-26
Citation
International Journal of Medical Robotics and Computer Assisted Surgery, 2023, 19 (3), pp.1-13
ISSN
1478-5951
Publisher
Wiley
Start Page
1
End Page
13
Journal / Book Title
International Journal of Medical Robotics and Computer Assisted Surgery
Volume
19
Issue
3
Copyright Statement
© 2023 The Authors. The International Journal of Medical Robotics and Computer Assisted Surgery published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000932989000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
2D/3D RECONSTRUCTION
3D imaging
3D RECONSTRUCTION
bone
CT
FEMORAL SHAPE
FLUOROSCOPIC X-RAY
joints
Life Sciences & Biomedicine
modelling
orthopaedic
PROXIMAL FEMUR
REGISTRATION
Science & Technology
statistical shape modelling
SURFACE MODEL
Surgery
VALIDATION
VERTEBRAL MODEL
Publication Status
Published
Article Number
e2503
Date Publish Online
2023-02-01