An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging

• Published in: Journal of digital imaging Vol. 37; no. 5; pp. 2428 - 2443
• Main Authors: Yıldız Potter, İlkay, Rodriguez, Edward K., Wu, Jim, Nazarian, Ara, Vaziri, Ashkan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2024; Springer Nature B.V
• Subjects: Automation; Body height; Bone diseases; Bone imaging; Compression; Computed tomography; Deep learning; Fractures; Image segmentation; Imaging; Injury analysis; Localization; Medical imaging; Medicine; Medicine & Public Health; Osteoporosis; Planet detection; Radiology; Segmentation; Sensitivity; Spine; Vertebrae; Deep learning; Osteoporosis; Computed tomography; Vertebral compression fracture
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01135-5

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs. Computed Tomography (CT) imaging is commonly used to detect occult injuries. Unlike the existing VCF detection approaches based on CT, the standard clinical criteria for determining VCF relies on the shape of vertebrae, such as loss of vertebral body height. We developed a novel automated vertebrae localization, segmentation, and osteoporotic VCF detection pipeline for CT scans using state-of-the-art deep learning models to bridge this gap. To do so, we employed a publicly available dataset of spine CT scans with 325 scans annotated for segmentation, 126 of which also graded for VCF (81 with VCFs and 45 without VCFs). Our approach attained 96% sensitivity and 81% specificity in detecting VCF at the vertebral-level, and 100% accuracy at the subject-level, outperforming deep learning counterparts tested for VCF detection without segmentation. Crucially, we showed that adding predicted vertebrae segments as inputs significantly improved VCF detection at both vertebral and subject levels by up to 14% Sensitivity and 20% Specificity ( p -value = 0.028).