Abstract
Osteophytes, a common degenerative change in the spine, are found in 90 % of the population over 60 years of age. We have developed an automated system to detect and assess spinal osteophytes on \(^{18}\)F-sodium fluoride (\(^{18}\)F-NaF) PET/CT studies. We first segment the cortical shell of the vertebral body and unwrap it to a 2D map. Multiple characteristic features derived from PET/CT images are then projected onto the map. Finally, we adopt a three-tier learning based scheme to compute a confidence map and detect osteophyte sites and clusters. The system was tested on 20 studies (10 training and 10 testing) and achieved 84 % sensitivity at 3.8 false positives per case for the training set, and 82 % sensitivity at a 4.7 false positive rate for the testing set.
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This work was supported by the Intramural Research Program at National Institutes of Health, Clinical Center.
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© 2014 Springer International Publishing Switzerland
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Yao, J., Munoz, H., Burns, J.E., Lu, L., Summers, R.M. (2014). Computer Aided Detection of Spinal Degenerative Osteophytes on Sodium Fluoride PET/CT. In: Yao, J., Klinder, T., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07269-2_5
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DOI: https://doi.org/10.1007/978-3-319-07269-2_5
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