Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Daniel Forsberg

ABSTRACT

Segmentation of the vertebrae in the spine is of relevance to many medical applications related to the spine. This paper describes a method based upon atlas-based registration for achieving an accurate segmentation of the thoracic and the lumbar vertebrae in the spine as imaged by computed tomography. The method has been evaluated on ten data sets provided as a part of the segmentation challenge hosted by the 2nd MICCAI workshop on Computational Methods and Clinical Applications for Spine Imaging. An average point-to-surface error of \(1.05\,\pm \,0.65\) mm and a mean DICE coefficient of \(0.94\,\pm \,0.03\) were obtained when comparing the computed segmentations with ground truth segmentations. These results are highly competitive when compared to the results of earlier presented methods. More... »

PAGES

49-59

References to SciGraph publications

  • 2010. Hierarchical Segmentation and Identification of Thoracic Vertebra Using Learning-Based Edge Detection and Coarse-to-Fine Deformable Model in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2010
  • 2008. Modality-Independent Determination of Vertebral Position and Rotation in 3D in MEDICAL IMAGING AND AUGMENTED REALITY
  • 2012. Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2009-02. A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration in JOURNAL OF MATHEMATICAL IMAGING AND VISION
  • 2013-12. An improved level set method for vertebra CT image segmentation in BIOMEDICAL ENGINEERING ONLINE
  • Book

    TITLE

    Recent Advances in Computational Methods and Clinical Applications for Spine Imaging

    ISBN

    978-3-319-14147-3
    978-3-319-14148-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-14148-0_5

    DOI

    http://dx.doi.org/10.1007/978-3-319-14148-0_5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1003540138


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