TY - JOUR
T1 - A unifying framework for automatic and semi-automatic segmentation of vertebrae from radiographs using sample-driven active shape models
AU - Mysling, Peter
AU - Petersen, Peter Kersten
AU - Nielsen, Mads
AU - Lillholm, Martin
PY - 2013/10
Y1 - 2013/10
N2 - 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.
AB - 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.
U2 - 10.1007/s00138-012-0460-2
DO - 10.1007/s00138-012-0460-2
M3 - Journal article
SN - 0932-8092
VL - 24
SP - 1421
EP - 1434
JO - Machine Vision and Applications
JF - Machine Vision and Applications
IS - 7
ER -