Automatic analysis of global spinal alignment from simple annotation of vertebral bodies

Sophia A. Doerr, Tharindu De Silva, Rohan Vijayan, Runze Han, Ali Uneri, Michael D. Ketcha, Xiaoxuan Zhang, Nishanth Khanna, Erick Westbroek, Bowen Jiang, Corinna Zygourakis, Nafi Aygun, Nicholas Theodore, Jeffrey H. Siewerdsen, Jeffrey H. Siewerdsen, Jeffrey H. Siewerdsen

Research output: Contribution to journalArticle

Abstract

Purpose: Measurement of global spinal alignment (GSA) is an important aspect of diagnosis and treatment evaluation for spinal deformity but is subject to a high level of inter-reader variability. Approach: Two methods for automatic GSA measurement are proposed to mitigate such variability and reduce the burden of manual measurements. Both approaches use vertebral labels in spine computed tomography (CT) as input: the first (EndSeg) segments vertebral endplates using input labels as seed points; and the second (SpNorm) computes a two-dimensional curvilinear fit to the input labels. Studies were performed to characterize the performance of EndSeg and SpNorm in comparison to manual GSA measurement by five clinicians, including measurements of proximal thoracic kyphosis, main thoracic kyphosis, and lumbar lordosis. Results: For the automatic methods, 93.8% of endplate angle estimates were within the inter-reader 95% confidence interval (CI95). All GSA measurements for the automatic methods were within the inter-reader CI95, and there was no statistically significant difference between automatic and manual methods. The SpNorm method appears particularly robust as it operates without segmentation. Conclusions: Such methods could improve the reproducibility and reliability of GSA measurements and are potentially suitable to applications in large datasets - e.g., for outcome assessment in surgical data science.

Original languageEnglish (US)
Article number035001
JournalJournal of Medical Imaging
Volume7
Issue number3
DOIs
StatePublished - May 1 2020

Keywords

  • global spinal alignment
  • image analysis
  • image-guided surgery
  • spine surgery
  • surgical data science
  • surgical outcomes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Doerr, S. A., De Silva, T., Vijayan, R., Han, R., Uneri, A., Ketcha, M. D., Zhang, X., Khanna, N., Westbroek, E., Jiang, B., Zygourakis, C., Aygun, N., Theodore, N., Siewerdsen, J. H., Siewerdsen, J. H., & Siewerdsen, J. H. (2020). Automatic analysis of global spinal alignment from simple annotation of vertebral bodies. Journal of Medical Imaging, 7(3), [035001]. https://doi.org/10.1117/1.JMI.7.3.035001