Background: Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecular attenuation values for opportunistic osteoporosis screening. Meanwhile, machine learning has been used to accurately segment large parts of the human skeleton. Purpose: To evaluate a fully automated deep learning-based method for lumbar vertebral segmentation and measurement of vertebral volumetric trabecular attenuation values. Material and methods: A deep learning-based method for automated segmentation of bones was retrospectively applied to non-contrast CT scans of 1008 patients (mean age 57 years, 472 female, 536 male). Each vertebral segmentation was automatically reduced by 7 mm in all directions in order to avoid cortical bone. The mean and median volumetric attenuation values from Th12 to L4 were obtained and plotted against patient age and sex. L1 values were further analyzed to facilitate comparison with previous studies. Results: The mean L1 attenuation values decreased linearly with age by −2.2 HU per year (age > 30, 95% CI: −2.4, −2.0, R2 = 0.3544). The mean L1 attenuation value of the entire population cohort was 140 HU ± 54. Conclusions: With results closely matching those of previous studies, we believe that our fully automated deep learning-based method can be used to obtain lumbar volumetric trabecular attenuation values which can be used for opportunistic screening of osteoporosis in patients undergoing CT scans for other reasons.
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