3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

  • Cheng Chen
  • D. Belavy
  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8679)


In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.


Intervertebral Disc Geometric Constraint Disc Center Training Point Sagittal Slice 
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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cheng Chen
    • 1
  • D. Belavy
    • 2
  • Guoyan Zheng
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernSwitzerland
  2. 2.Department of RadiologyCharite University Medicine BerlinGermany

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