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

  • Jianhua YaoEmail author
  • Shuo Li
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)

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.

Keywords

Lumbar Spine Segmentation Result Shape Model Transverse Process Dice Coefficient 
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.

Notes

Acknowledgments

We thank Dr. Joseph Burns in the Department of Radiological Sciences, University of California, Irvine, Medical Center for providing the CT data set. We thank Dr. Sasha Getty and Mr. James Stieger for providing the manual segmentation for the reference data. We also thank Dr. Ronald Summers in the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, NIH for providing the resource to carry out the evaluation. We also thank SpineWeb established by Digital Imaging Group of London for hosting the publicly available data set.

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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2015

Authors and Affiliations

  1. 1.Imaging Biomarkers and Computer-Aided Detection Laboratory, Radiology and Imaging SciencesNational Institutes of Health Clinical CenterBethesdaUSA
  2. 2.GE HealthcareMississaugaCanada
  3. 3.University of Western OntarioLondonCanada

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