A Deep-Learning-Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images.

Abstract

The paraspinal muscles have been extensively studied on axial lumbar magnetic resonance imaging (MRI) for better understanding of back pain; however, the acquisition of measurements mainly relies on manual segmentation, which is time consuming. The study objective was to develop and validate a deep-learning-based program for automated acquisition of quantitative measurements for major lumbar spine components on axial lumbar MRIs, the paraspinal muscles in particular. This study used a cross-sectional observational design. From the Hangzhou Lumbar Spine Study, T2-weighted axial MRIs at the L4-5 disk level of 120 participants (aged 54.8 years [SD=15.0]) were selected to develop the deep-learning-based program Spine Explorer (Tulong). Another 30 axial lumbar MRIs were automatically measured by Spine Explorer and then manually measured using ImageJ to acquire quantitative size and compositional measurements for bilateral multifidus, erector spinae, and psoas muscles; the disk; and the spinal canal. Intersection-over-union and Dice score were used to evaluate the performance of automated segmentation. Intraclass coefficients and Bland-Altman plots were used to examine intersoftware agreements for various measurements. After training, Spine Explorer (Tulong) measures an axial lumbar MRI in 1second. The intersections-over-union were 83.3% to 88.4% for the paraspinal muscles and 92.2% and 82.1% for the disk and spinal canal, respectively. For various size and compositional measurements of paraspinal muscles, Spine Explorer (Tulong) was in good agreement with ImageJ (intraclass coefficient =0.85to approximately0.99). Spine Explorer (Tulong) is automated, efficient, and reliable in acquiring quantitative measurements for the paraspinal muscles, the disk, and the canal, and various size and compositional measurements were simultaneously obtained for the lumbar paraspinal muscles. Such an automated program might encourage further epidemiological studies of the lumbar paraspinal muscle degeneration and enhance paraspinal muscle assessment in clinical practice.