title: Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, South Yorkshire, S1 3JD, UK. isaac.casm@sheff.ac.uk.
authors.forename: Isaac
authors.identifier: 0000-0002-2745-426X
authors.initials: I
authors.lastname: Castro-Mateos
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, South Yorkshire, S1 3JD, UK.
authors.forename: Rui
authors.initials: R
authors.lastname: Hua
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, South Yorkshire, S1 3JD, UK.
authors.forename: Jose M
authors.initials: JM
authors.lastname: Pozo
authors.affiliation: National Center for Spinal Disorders (NCSD), Budapest, Hungary.
authors.forename: Aron
authors.initials: A
authors.lastname: Lazary
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, South Yorkshire, S1 3JD, UK.
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007403:Intervertebral Disc / Q000000981:diagnostic imaging / Q000473:pathology; D055959:Intervertebral Disc Degeneration / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity
keywords: 2D; Active contour model; Automatic; Classification; Degeneration; IVD; MR; Neural network