A unifying framework for automatic and semi-automatic segmentation of vertebrae from radiographs using sample-driven active shape models |
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Authors: | Peter Mysling Kersten Petersen Mads Nielsen Martin Lillholm |
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Affiliation: | 1. eScience Center, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen ?, Denmark 2. Biomediq A/S, Fruebjergvej 3, 2100, Copenhagen ?, Denmark
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Abstract: | Segmentation of vertebral contours is an essential task in the design of imaging biomarkers for osteoporosis based on vertebra shape or texture. In this paper, we propose a novel automatic segmentation technique which can optionally be constrained by the user. The proposed technique solves the segmentation problem in a hierarchical manner. In the first phase, a coarse estimate of the overall spine alignment and the vertebra locations is computed using a sampling scheme. These samples are used to initialize a second phase of active shape model search, under a nonlinear model of vertebra appearance. The search is constrained by a conditional shape model, based on the variability of the coarse spine location estimates. In supplement, we describe an approach for manual initialization of the segmentation procedure as a simple set of constraints on the fully automatic technique. The technique is evaluated on a data base of 157 manually annotated lumbar radiographs, resulting in a final mean point-to-contour error of $0.81~\pm ~0.98$ mm for automatic segmentation. The results outperform the previous work in automatic vertebra segmentation in terms of both segmentation accuracy and failure rate, offering a both automatic and semi-automatic approach in one unifying framework. |
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