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

Automatic segmentation of the thumb trapeziometacarpal joint using parametric statistical shape modelling and random forest regression voting. Computer methods in biomechanics and biomedical engineering. Imaging & visualization Schneider, M. T., Zhang, J., Crisco, J. J., Weiss, A. C., Ladd, A. L., Nielsen, P. M., Besier, T. 2019; 7 (3): 297-301

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 (RFRV) with statistical shape model (SSM) 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 (RMS) errors of 1.066 mm and 0.632 mm for the first metacarpal and trapezial bones respectively, and a segmentation time of ~2 minutes per CT image, the preliminary results showed promise for providing accurate 3D meshes of TMC joint bones for batch processing.

View details for DOI 10.1080/21681163.2018.1501765

View details for PubMedID 31275767

View details for PubMedCentralID PMC6608596