Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging

Abstract

Background: Computer-aided diagnosis (CAD) methods for detecting and classifying lumbar spine disease in Magnetic Resonance imaging (MRI) can assist radiologists to perform their decision-making tasks. In this paper, a CAD software has been developed able to classify and quantify spine disease (disc degeneration, herniation and spinal stenosis) in two-dimensional MRI.

Methods: A set of 52 lumbar discs from 14 patients was used for training and 243 lumbar discs from 53 patients for testing in conventional two-dimensional MRI of the lumbar spine. To classify disc degeneration according to the gold standard, Pfirrmann classification, a method based on the measurement of disc signal intensity and structure was developed. A gradient Vector Flow algorithm was used to extract disc shape features and for detecting contour abnormalities. Also, a signal intensity method was used for segmenting and detecting spinal stenosis. Novel algorithms have also been developed to quantify the severity of these pathologies. Variability was evaluated by kappa (k) and intra-class correlation (ICC) statistics.

Results: Segmentation inaccuracy was below 1%. Almost perfect agreement, as measured by the k and ICC statistics, was obtained for all the analyzed pathologies: disc degeneration (k=0.81 with 95% CI= [0.75..0.881) with a sensitivity of 95.8% and a specificity of 92.6%, disc herniation (k=0.94 with 95% CI= [0.87..1]) with a sensitivity of 60% and a specificity of 87.1%, categorical stenosis (k=0.94 with 95% CI= [0.90..0.98]) and quantitative stenosis (ICC=0.98 with 95% Cl= [0.97..0.981) with a sensitivity of 70% and a specificity of 81.7%.

Discussion: The proposed methods are reproducible and should be considered as a possible alternative when compared to reference standards.