Abstract: A method is disclosed for fully automated segmentation of human vertebral body images in a CT (computerized tomography) study with no user interaction and no phantoms, which has resiliency to anatomical abnormalities, and protocol and scanner variations. The method was developed to enable automated detection of osteoporosis in CT studies performed for other clinical reasons. Testing with 1,044 abdominal CTs from multiple sites, resulted in detection of 96.3% of the vertebral bodies and 1% false positives. Of the detected vertebral bodies, 83.3% were segmented adequately for sagittal plane quantitative evaluation of vertebral fractures indicative of osteoporosis. Improved results were observed when selecting the best sagittal plane of 3 for each vertebra, yielding a segmentation success rate of 85.4%. The method is preferably implemented in software as a building block in a system for automated osteoporosis detection.

Abstract: A method Is disclosed for fully automated segmentation of human vertebral body images in a CT (computerized tomography) study with no user interaction and no phantoms, which has resiliency to, anatomical abnormalities, and protocol and scanner variations. The method was developed to enable automated detection of osteoporosis in CT studies performed for other clinical reasons. Testing with 1.044 abdominal CTs from multiple sites, resulted in detection of 96.3% of the vertebral, bodies and 1% false positives. Of the detected vertebral bodies, 83.3% were segmented adequately for sagittal plane quantitative evaluation of vertebral, fractures indicative of osteoporosis. Improved results were observed when selecting the best sagittal plane of 3 for each vertebra, yielding a segmentation success rate of 85.4%. The method is preferably implemented in software as a building block in a system for automated osteoporosis detection.