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

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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.
Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging : Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings
EditorsKenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan
Number of pages8
Publication date2011
ISBN (Print)978-3-642-24318-9
ISBN (Electronic)978-3-642-24319-6
Publication statusPublished - 2011
EventInternational Workshop on Machine Learning in Medical Imaging - Toronto, Canada
Duration: 18 Sep 201118 Sep 2011
Conference number: 2


ConferenceInternational Workshop on Machine Learning in Medical Imaging
SeriesLecture notes in computer science

Bibliographical note

Del af 14th International Conference on Medical
Image Computing and Computer-Assisted Intervention (MICCAI)

ID: 168782252