Automatic Multi-modal Cervical Spine Image Atlas Segmentation

Using Adaptive Stochastic Gradient Descent
  • Ibraheem Al-Dhamari
  • Sabine Bauer
  • Dietrich Paulus
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

A personalized medicine has been advanced in different fields of medicine to combat, control and prevent a number of diseases. In personalized medicine, products are customized and only suitable for a specific patient. In spinal surgery, medical images are taken into account to implant spinal devices with the aim of minimizing the risk of insufficient implant fit. A model of the spine is generated from these images and used in biomechanic framework to simulate the effect of the customized implant on a specific patient.

To generate such a model, an efficient and practical segmentation method is needed which is proposed in this paper. The large deformation of human spine and the touching boundaries of neighboring vertebrae make the problem of spine segmentation very challenging. The classical segmentation methods e.g. thresholding or region growing fail to separate different vertebrae. The state of the art methods using shape models require a long time for training and testing. A new method for automatic multi-modal cervical spine segmentation is proposed in this paper. The proposed method requires only a few seconds to segment a specific vertebra or the whole cervical spine. It is provided as Slicer 3D plug-in which is free and open-source. The public datasets available fail to provide high quality MRI cervical spine images. Another contribution of this study is providing a high quality multi-modal cervical spine public and free dataset.

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Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Ibraheem Al-Dhamari
    • 1
  • Sabine Bauer
    • 1
  • Dietrich Paulus
    • 1
  1. 1.Medizinische Tekchnik Institut (MTI)Koblenz and Landau UniversityMainzDeutschland

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