Automatic generation of statistical pose and shape models for articulated joints

Chen, Xin and Graham, Jim and Hutchinson, Charles and Muir, Lindsay (2013) Automatic generation of statistical pose and shape models for articulated joints. IEEE Transactions on Medical Imaging, 33 (2). pp. 372-383. ISSN 1558-254X

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Abstract

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

Item Type: Article
Additional Information: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Wrist, Carpal bones, 3D image registration, Segmentation, Statistical pose model, Statistical shape model, Articulated joint
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1109/TMI.2013.2285503
Depositing User: Chen, Xin
Date Deposited: 21 Apr 2017 13:57
Last Modified: 21 Apr 2017 15:29
URI: http://eprints.nottingham.ac.uk/id/eprint/42106

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