• Benjamin Dourthe, Noor Shaikh, Pai SAnooshaADepartment of Orthopaedics, University of British Columbia, Vancouver, BC, Canada.School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada., Sidney Fels, BrownStephen H MSHMDepartment of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON, Canada., David R Wilson, John Street, and Thomas R Oxland.
• ICORD, Blusson Spinal Cord Centre, University of British Columbia, Vancouver, BC, Canada.
• Spine. 2022 Aug 15; 47 (16): 117911861179-1186.

Study DesignRandomized trial.ObjectiveTo implement an algorithm enabling the automated segmentation of spinal muscles from open magnetic resonance images in healthy volunteers and patients with adult spinal deformity (ASD).Summary Of Background DataUnderstanding spinal muscle anatomy is critical to diagnosing and treating spinal deformity.Muscle boundaries can be extrapolated from medical images using segmentation, which is usually done manually by clinical experts and remains complicated and time-consuming.MethodsThree groups were examined: two healthy volunteer groups (N = 6 for each group) and one ASD group (N = 8 patients) were imaged at the lumbar and thoracic regions of the spine in an upright open magnetic resonance imaging scanner while maintaining different postures (various seated, standing, and supine). For each group and region, a selection of regions of interest (ROIs) was manually segmented. A multiscale pyramid two-dimensional convolutional neural network was implemented to automatically segment all defined ROIs. A five-fold crossvalidation method was applied and distinct models were trained for each resulting set and group and evaluated using Dice coefficients calculated between the model output and the manually segmented target.ResultsGood to excellent results were found across all ROIs for the ASD (Dice coefficient >0.76) and healthy (dice coefficient > 0.86) groups.ConclusionThis study represents a fundamental step toward the development of an automated spinal muscle properties extraction pipeline, which will ultimately allow clinicians to have easier access to patient-specific simulations, diagnosis, and treatment.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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