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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 141–148Cite as

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Robust MR Spine Detection Using Hierarchical Learning and Local Articulated Model

Robust MR Spine Detection Using Hierarchical Learning and Local Articulated Model

  • Yiqiang Zhan19,
  • Dewan Maneesh19,
  • Martin Harder20 &
  • …
  • Xiang Sean Zhou19 
  • Conference paper
  • 5784 Accesses

  • 31 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

A clinically acceptable auto-spine detection system, i.e., localization and labeling of vertebrae and inter-vertebral discs, is required to have high robustness, in particular to severe diseases (e.g. scoliosis) and imaging artifacts (e.g. metal artifacts in MR). Our method aims to achieve this goal with two novel components. First, instead of treating vertebrae/discs as either repetitive components or completely independent entities, we emulate a radiologist and use a hierarchial strategy to learn detectors dedicated to anchor (distinctive) vertebrae, bundle (non-distinctive) vertebrae and inter-vertebral discs, respectively. At run-time, anchor vertebrae are detected concurrently to provide redundant and distributed appearance cues robust to local imaging artifacts. Bundle vertebrae detectors provide candidates of vertebrae with subtle appearance differences, whose labels are mutually determined by anchor vertebrae to gain additional robustness. Disc locations are derived from a cloud of responses from disc detectors, which is robust to sporadic voxel-level errors. Second, owing to the non-rigidness of spine anatomies, we employ a local articulated model to effectively model the spatial relations across vertebrae and discs. The local articulated model fuses appearance cues from different detectors in a way that is robust to abnormal spine geometry resulting from severe diseases. Our method is validated by 300 MR spine scout scans and exhibits robust performance, especially to cases with severe diseases and imaging artifacts.

Keywords

  • Scoliotic Spine
  • Magnetic Resonance Spine
  • Spine Anatomy
  • Slice Group
  • Hierarchical Learn

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Author information

Authors and Affiliations

  1. Siemens Medical Solutions USA, Inc., Malvern, USA

    Yiqiang Zhan, Dewan Maneesh & Xiang Sean Zhou

  2. Siemens Healthcare Imaging MR, Erlangen, German

    Martin Harder

Authors
  1. Yiqiang Zhan
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  2. Dewan Maneesh
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  3. Martin Harder
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  4. Xiang Sean Zhou
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Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhan, Y., Maneesh, D., Harder, M., Zhou, X.S. (2012). Robust MR Spine Detection Using Hierarchical Learning and Local Articulated Model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_18

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  • DOI: https://doi.org/10.1007/978-3-642-33415-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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