Vertebra segmentation based on two-step refinement View Full Text


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Article Info

DATE

2016-07-26

AUTHORS

Jean-Baptiste Courbot, Edmond Rust, Emmanuel Monfrini, Christophe Collet

ABSTRACT

Knowledge of vertebra location, shape, and orientation is crucial in many medical applications such as orthopedics or interventional procedures. Computed tomography (CT) offers a high contrast between bone and soft tissues, but automatic vertebra segmentation remains difficult. Hence, the wide range of shapes, aging, and degenerative joint disease alterations as well as the variety of pathological cases encountered in an aging population make automatic segmentation sometimes challenging. Besides, daily practice implies a need for affordable computation time. This paper aims to present a new automated vertebra segmentation method (using a first bounding box for initialization) for CT 3D data which tackles these problems. This method is based on two consecutive steps. The first one is a new coarse-to-fine method efficiently reducing the data amount to obtain a coarse shape of the vertebra. The second step consists in a hidden Markov chain (HMC) segmentation using a specific volume transformation within a Bayesian framework. Our method does not introduce any prior on the expected shape of the vertebra within the bounding box and thus deals with the most frequent pathological cases encountered in daily practice. We experiment this method on a set of standard lumbar, thoracic, and cervical vertebrae and on a public dataset, on pathological cases, and in a simple integration example. Quantitative and qualitative results show that our method is robust to changes in shapes and luminance and provides correct segmentation with respect to pathological cases. More... »

PAGES

1

References to SciGraph publications

  • 2005. A New Coarse-to-Fine Framework for 3D Brain MR Image Registration in COMPUTER VISION FOR BIOMEDICAL IMAGE APPLICATIONS
  • 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
  • 2012. Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2012. Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2001-06. Contour and Texture Analysis for Image Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2005. Method for Automatically Segmenting the Spinal Cord and Canal from 3D CT Images in COMPUTER ANALYSIS OF IMAGES AND PATTERNS
  • 1994. Space-Filling Curves in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40244-016-0018-0

    DOI

    http://dx.doi.org/10.1186/s40244-016-0018-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005291305

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27512644


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