Imperial College London
Portrait Picture

Mr. Gareth G. Jones

Faculty of Medicine, Department of Surgery & Cancer

Clinical Senior Lecturer in Orthopaedic Surgery
 
 
 
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Contact

 

+44 (0)20 7594 5465g.g.jones

 
 
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Location

 

203Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Patil:2023:10.1002/rcs.2503,
author = {Patil, A and Kulkarni, K and Xie, S and Bull, AMJ and Jones, GG},
doi = {10.1002/rcs.2503},
journal = {International Journal of Medical Robotics and Computer Assisted Surgery},
pages = {1--13},
title = {The accuracy of statistical shape models in predicting bone shape: a systematic review},
url = {http://dx.doi.org/10.1002/rcs.2503},
volume = {19},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundThis systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.MethodsA 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.Results2127 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).ConclusionStatistical 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.
AU  - Patil,A
AU  - Kulkarni,K
AU  - Xie,S
AU  - Bull,AMJ
AU  - Jones,GG
DO  - 10.1002/rcs.2503
EP  - 13
PY  - 2023///
SN  - 1478-5951
SP  - 1
TI  - The accuracy of statistical shape models in predicting bone shape: a systematic review
T2  - International Journal of Medical Robotics and Computer Assisted Surgery
UR  - http://dx.doi.org/10.1002/rcs.2503
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000932989000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://onlinelibrary.wiley.com/doi/10.1002/rcs.2503
UR  - http://hdl.handle.net/10044/1/104285
VL  - 19
ER  -
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