Abstract
BackgroundThe imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique.MethodsMagnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures.ResultsThere was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm.ConclusionThe proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method.
Highlights
The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP)
Inter‐method agreement Figures 2 and 3 show the combined Bland and Altman 95% limits of agreement plots for the functional cross-sectional area (FCSA) and fat percentage measurement from the right multifidus and erector spinae at L4–L5 and L5–S1 using the first set of measurements collected using the manual thresholding technique and automated algorithm
The region of interest (ROI) of interest is first manually segmented and the algorithm computes the muscle total cross-sectional area (CSA), fat CSA and fat percentage automatically. This novel algorithm was validated against paraspinal muscle composition measurements obtained using an established, highly reliable manual thresholding method, on a sample representing a clinically relevant population with chronic LBP
Summary
The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). The reliability of measurements of lumbar multifidus fatty infiltration, using the Goutallier classification system (GCS) (0–4 grading scale) [8], which was initially developed to assess fatty degeneration in rotator cuff injuries, was assessed Such studies have reported good intra-rater (ICC or kappa 0.71–0.93) [8,9,10] and inter-rater reliability (ICC or kappa 0.58–0.85) [8,9,10], these methods do not provide precise measurement and are not suitable to evaluate changes over time. The tedious and rater-dependent nature of such manual thresholding techniques for paraspinal muscle composition assessment provides the impetus for the development of automated or semi-automated segmentation methods. The threshold values in the interactive segmentation technique, are based on visual inspection and, remain rater-dependent
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