Spinal degeneration and vertebral fractures are common among the elderly adversely impacting mobility, quality of life, lung function, fracture risk, and mortality. Segmentation of individual vertebrae from computed tomography (CT) imaging is crucial for studying spine degeneration, vertebral fractures, and bone density with aging and their mechanistic links with demographics, lifestyle factors, and comorbidities. We present an automated method to segment individual vertebral bodies (T1-L1) and compute the kyphotic angle of the spine from chest CT images. A three-dimensional U-Net was developed, optimized, and trained to generate a voxellevel vertebral probability map from a chest CT scan. Multi-parametric thresholding was applied on the probability map to segment individual vertebrae by iteratively relaxing the probability threshold value, while avoiding fusion among adjacent vertebrae. The kyphotic angle was computed using two orthogonal planes on the spine centerline at the inter-vertebral spaces T3-T4 and T12-L1 and a common sagittal plane. Total lung capacity (TLC) chest CT scans from baseline visits of the COPDGene Iowa cohort were used for our experiments. The U-Net method was trained and validated using 40 scans and tested on a separate set of 100 scans. Segmentation of individual vertebrae achieved a mean Dice score of 0.93 as compared to manual segmentation, and the kyphotic angle computation method produced a linear correlation of 0.88 (r-value) with manual measurements. This method provides a fully automated tool to study different mechanistic pathways of age-related spine modeling and vertebral fractures in retrospective datasets available from large multi-site chest related studies.
Current clinical chest CT reporting includes limited qualitative assessment of emphysema with rare mention of lung volumes and limited reporting of emphysema, based upon retrospective review of CT reports. Quantitative CT analysis performed in COPDGene and other research cohorts utilize semiautomated segmentation procedures and well-established research method (Thirona). We compared this reference QCT data with fully automated QCT analysis that can be obtained at the time of CT scan and sent to PACS along with standard chest CT images. 164 COPDGene® cohort study subjects enrolled at Brigham and Women’s Hospital had baseline and 5-year follow-up CT scans. Subjects included 17 nonsmoking controls, 92 smokers with normal spirometry, 15 preserved ratio impaired spirometry (PRISm) patients, 12 GOLD 1, 20 GOLD 2, and 8 GOLD 3–4. 97% (n = 319) of clinical reports did not mention lung volumes, and 14% (n = 46) made no mention of emphysema. Total lung volumes determined by the fully automated algorithm were consistently 47 milliliters (ml) less than the Thirona reference value for all subjects (95% confidence interval −62 to −32 ml). Percent emphysema values were equivalent to the Thirona reference values. Well-established research reference data can be used to evaluate and validate automated QCT software. Validation can be repeated as software is updated.
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