Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting

ABSTRACT

We propose an automatic pipeline for creating shape modelling suitable parametric meshes of the trapeziometacarpal (TMC) joint from clinical CT images for the purpose of batch processing and analysis. The method uses 3D random forest regression voting with statistical shape model segmentation. The method was demonstrated in a validation experiment involving 65 CT images, 15 of which were randomly selected to be excluded from the training set for testing. With mean root mean squared errors of 1.066 and 0.632 mm for the first metacarpal and trapezial bones, respectively, and a segmentation time of ~2 min per CT image, the preliminary results showed promise for providing accurate 3D meshes of TMC joint bones for batch processing.

KEYWORDS:

• Automatic segmentation
• statistical shape model
• random forest
• regression voting
• trapeziometacarpal
• model generation

Acknowledgements

This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health (Award number AR059185) as well as the Auckland Bioengineering Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://scikit-learn.org/stable/.

Additional information

Funding

This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health (Award number AR059185) as well as the Auckland Bioengineering Institute.