Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach

TY - GEN

T1 - Automatic segmentation of vertebrae from radiographs

T2 - International Workshop on Machine Learning in Medical Imaging

AU - Mysling, Peter

AU - Petersen, Peter Kersten

AU - Nielsen, Mads

AU - Lillholm, Martin

N1 - Conference code: 2

PY - 2011

Y1 - 2011

N2 - Segmentation of vertebral contours is an essential task in the design of automatic tools for vertebral fracture assessment. In this paper, we propose a novel segmentation technique which does not require operator interaction. The proposed technique solves the segmentation problem in a hierarchical manner. In a first phase, a coarse estimate of the overall spine alignment and the vertebra locations is computed using a shape model 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. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation accuracy and failure rate.

AB - Segmentation of vertebral contours is an essential task in the design of automatic tools for vertebral fracture assessment. In this paper, we propose a novel segmentation technique which does not require operator interaction. The proposed technique solves the segmentation problem in a hierarchical manner. In a first phase, a coarse estimate of the overall spine alignment and the vertebra locations is computed using a shape model 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. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation accuracy and failure rate.

U2 - 10.1007/978-3-642-24319-6_2

DO - 10.1007/978-3-642-24319-6_2

M3 - Article in proceedings

SN - 978-3-642-24318-9

T3 - Lecture notes in computer science

SP - 10

EP - 17

BT - Machine Learning in Medical Imaging

A2 - Suzuki, Kenji

A2 - Wang, Fei

A2 - Shen, Dinggang

A2 - Yan, Pingkun

PB - Springer

Y2 - 18 September 2011 through 18 September 2011

ER -