Use of machine learning to select texture features in investigating the effects of axial loading on T2-maps from magnetic resonance imaging of the lumbar discs

• Published in: European spine journal 2022-08, Vol.31 (8), p.1979-1991
• Main Authors: Abdollah, Vahid, Parent, Eric C., Dolatabadi, Samin, Marr, Erica, Wachowicz, Keith, Battié, Michele
• Format: Article
• Language: English
• Subjects: Back pain; Biomechanics; Compression; Degenerative disc disease; Image processing; Intervertebral discs; Investigations; Learning algorithms; Machine learning; Magnetic resonance imaging; Medicine; Medicine & Public Health; Neurosurgery; Original Article; Osteoporosis; Spinal cord; Statistical analysis; Surgical Orthopedics
• ISSN: 0940-6719; 1432-0932
• DOI: 10.1007/s00586-021-07036-3

Background Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc’s response to loading. Methods Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T 2 -weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen’s d compared loading conditions. Findings All statistically significant differences ( p