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European Spine Journal

, Volume 25, Issue 9, pp 2721–2727 | Cite as

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

  • Isaac Castro-MateosEmail author
  • Rui Hua
  • Jose M. Pozo
  • Aron Lazary
  • Alejandro F. Frangi
Original Article

Abstract

Purpose

The primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann’s degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties.

Materials and methods

T2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann’s grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs.

Results

The classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth.

Conclusions

Our results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI.

Keywords

Classification IVD Degeneration MR 2D Automatic Active contour model Neural network 

Notes

Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement number 269909, MySpine project.

Compliance with ethical standards

Conflict of interest

None of the authors has any potential conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Electronic and Electrical EngineeringThe University of SheffieldSheffieldUK
  2. 2.National Center for Spinal Disorders (NCSD)BudapestHungary

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