Automated Segmentation of Spinal Muscles From Upright Open MRI Using a Multiscale Pyramid 2D Convolutional Neural Network. Issue 16 (15th August 2022)

• Record Type: Journal Article
• Authors: Dourthe, Benjamin; Shaikh, Noor; Pai S., Anoosha; Fels, Sidney; Brown, Stephen H.M.; Wilson, David R.; Street, John; Oxland, Thomas R.
• Is Part Of: Spine. Volume 47:Issue 16(2022)
• Page Start: 1179
• Page End: 1186
• Publication Date: 2022-08-15
• Subjects: automated segmentation -- computer-assisted diagnosis -- deep learning -- image processing -- image segmentation -- magnetic resonance imaging -- medical imaging -- orthopedic -- spinal deformity.; 2; Spine -- Abnormalities -- Periodicals; Spine -- Diseases -- Periodicals; Spine -- Surgery -- Periodicals; 616.73005
• DOI: 10.1097/BRS.0000000000004308
• Languages: English
• ISSNs: 0362-2436

Abstract

Abstract : Study Design: Randomized trial. Objective: To 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 Data: Understanding 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. Methods: Three 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. Results: Good to excellent results were found across all ROIs for the ASD (Dice coefficient >0.76) and healthy (dice coefficient > 0.86) groups. Conclusion: This 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.