3D Vertebra Segmentation by Feature Selection Active Shape Model

  • Isaac Castro-MateosEmail author
  • Jose M. Pozo
  • Aron Lazary
  • Alejandro Frangi
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)


In this paper, a former method has been adapted to perform vertebra segmentations for the 2nd Workshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014). A statistical Shape Models (SSM) of each lumbar vertebra was created for the segmentation step. From manually placed intervertebral discs centres, the similarity parameters are computed to initialise the vertebra shapes. The segmentation is performed by iteratively deforming a mesh inside the image intensity and then projecting it into the SSM space until convergence. Afterwards, a relaxation step based on B-spline is applied to overcome the SSM rigidity. The deformation of the mesh, within the image intensity, is performed by displacing each landmark along the normal direction of the surface mesh at the landmark position seeking a minimum of a cost function based on a set of trained features. The organisers tested the performance of our method with a dataset of five patients, achieving a global mean Dice Similarity Index (DSI) of 93.4 %. Results were consistent and accurate along the lumbar spine 93.8, 93.9, 93.7, 93.4 and 92.1 %, from L1 to L5.


Lumbar Spine Intervertebral Disc Testing Dataset Articular Process Active Shape Model 
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.


  1. 1.
    Roberts, M.G., Cootes, T.F., Pacheco, E., Oh, T., Adams, J.E.: Segmentation of lumbar vertebrae using part-based graphs and active appearance models. In: Lecture notes in computer science (MICCAI), pp. 1017–1024. Springer (2009)Google Scholar
  2. 2.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. Med. Image Anal. 13(3), 471 (2009)CrossRefGoogle Scholar
  3. 3.
    Weese, J., Kaus, M., Lorenz, C., Lobregt, S., Truyen, R., Pekar, V.: Shape constrained deformable models for 3D medical image segmentation. Inf. Process. Med. Imaging pp. 380–387 (2001)Google Scholar
  4. 4.
    Castro-Mateos, I., Pozo, J.M., del Rio, L., Eltes, P., Lazary, A., Frangi, A.F.: 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images. Manuscript accepted for publication in Physics in Medicine and Biology (2014)Google Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Graham, J.: Active shape models- their training and applications. Comput. Vis. Image Under 61(1), 38–59 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Isaac Castro-Mateos
    • 1
    Email author
  • Jose M. Pozo
    • 1
  • Aron Lazary
    • 2
  • Alejandro Frangi
    • 1
  1. 1.Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Mechanical EngineeringUniversity of SheffieldSheffieldUK
  2. 2.National Center for Spine Disorder (NCSD)BudapestHungary

Personalised recommendations