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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Automatic segmentation of vertebrae from radiographs : a sample-driven active shape model approach. / Mysling, Peter; Petersen, Peter Kersten; Nielsen, Mads; Lillholm, Martin.

Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. red. / Kenji Suzuki; Fei Wang; Dinggang Shen; Pingkun Yan. Springer, 2011. s. 10-17 (Lecture notes in computer science, Bind 7009).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Mysling, P, Petersen, PK, Nielsen, M & Lillholm, M 2011, Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach. i K Suzuki, F Wang, D Shen & P Yan (red), Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Springer, Lecture notes in computer science, bind 7009, s. 10-17, International Workshop on Machine Learning in Medical Imaging, Toronto, Canada, 18/09/2011. https://doi.org/10.1007/978-3-642-24319-6_2

APA

Mysling, P., Petersen, P. K., Nielsen, M., & Lillholm, M. (2011). Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach. I K. Suzuki, F. Wang, D. Shen, & P. Yan (red.), Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings (s. 10-17). Springer. Lecture notes in computer science Bind 7009 https://doi.org/10.1007/978-3-642-24319-6_2

Vancouver

Mysling P, Petersen PK, Nielsen M, Lillholm M. Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach. I Suzuki K, Wang F, Shen D, Yan P, red., Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Springer. 2011. s. 10-17. (Lecture notes in computer science, Bind 7009). https://doi.org/10.1007/978-3-642-24319-6_2

Author

Mysling, Peter ; Petersen, Peter Kersten ; Nielsen, Mads ; Lillholm, Martin. / Automatic segmentation of vertebrae from radiographs : a sample-driven active shape model approach. Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. red. / Kenji Suzuki ; Fei Wang ; Dinggang Shen ; Pingkun Yan. Springer, 2011. s. 10-17 (Lecture notes in computer science, Bind 7009).

Bibtex

@inproceedings{62919a845a894332bd3557e5ce038186,
title = "Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach",
abstract = "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. ",
author = "Peter Mysling and Petersen, {Peter Kersten} and Mads Nielsen and Martin Lillholm",
note = "Del af 14th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI); International Workshop on Machine Learning in Medical Imaging, MLMI ; Conference date: 18-09-2011 Through 18-09-2011",
year = "2011",
doi = "10.1007/978-3-642-24319-6_2",
language = "English",
isbn = "978-3-642-24318-9",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "10--17",
editor = "Kenji Suzuki and Fei Wang and Dinggang Shen and Pingkun Yan",
booktitle = "Machine Learning in Medical Imaging",
address = "Switzerland",

}

RIS

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 -

ID: 168782252