Report of Vertebra Segmentation Challenge in 2014 MICCAI Workshop on Computational Spine Imaging View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Jianhua Yao , Shuo Li

ABSTRACT

Segmentation is the fundamental step for most spine image analysis tasks. The vertebra segmentation challenge held at the 2014 Computational Spine Imaging Workshop (CSI2014) objectively evaluated the performance of several algorithms segmenting vertebrae in spine CT scans. Five teams participated in the challenge. Ten training data sets and Five test data sets with reference annotation were provided for training and evaluation. Dice coefficient and absolute surface distances were used as the evaluation metrics. The segmentations on both the whole vertebra and its substructures were evaluated. The performances comparisons were assessed in different aspects. The top performers in the challenge achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine. The strength and weakness of each method are discussed in this paper. More... »

PAGES

247-259

References to SciGraph publications

  • 2015. Interpolation-Based Shape-Constrained Deformable Model Approach for Segmentation of Vertebrae from CT Spine Images in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 2015. Vertebrae Segmentation in 3D CT Images Based on a Variational Framework in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 2013-12. An improved level set method for vertebra CT image segmentation in BIOMEDICAL ENGINEERING ONLINE
  • 2015. 3D Vertebra Segmentation by Feature Selection Active Shape Model in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 2015. Atlas-Based Segmentation of the Thoracic and Lumbar Vertebrae in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 2015. Lumbar and Thoracic Spine Segmentation Using a Statistical Multi-object Shape $$+$$ Pose Model in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 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. Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2009. Segmentation of Lumbar Vertebrae Using Part-Based Graphs and Active Appearance Models in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 2015. Interpolation-Based Detection of Lumbar Vertebrae in CT Spine Images in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 2015. Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data in RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING
  • 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_23

    DOI

    http://dx.doi.org/10.1007/978-3-319-14148-0_23

    DIMENSIONS

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