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Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

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Computational Radiology for Orthopaedic Interventions

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 23))

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Abstract

This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Southeast University, Nanjing, China

    Xiao Dong

  2. Institute for Surgical Technology and Biomechanics, Bern University, Bern, Switzerland

    Guoyan Zheng

Authors
  1. Xiao Dong
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  2. Guoyan Zheng
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Correspondence to Xiao Dong .

Editor information

Editors and Affiliations

  1. Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland

    Guoyan Zheng

  2. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

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Dong, X., Zheng, G. (2016). Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets. In: Zheng, G., Li, S. (eds) Computational Radiology for Orthopaedic Interventions. Lecture Notes in Computational Vision and Biomechanics, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-23482-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-23482-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23481-6

  • Online ISBN: 978-3-319-23482-3

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