Automation of Spine Curve Assessment in Frontal Radiographs Using Deep Learning of Vertebral-Tilt Vector

Kang Cheol Kim, Hye Sun Yun, Sungjun Kim, Jin Keun Seo

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


In this paper, an automated and visually explainable system is proposed for a scoliosis assessment from spinal radiographs, which deals with the drawback of manual measurements, which are known to be time-consuming, cumbersome, and operator dependent. Deep learning techniques have been successfully applied in the accurate extraction of Cobb angle measurements, which is the gold standard for a scoliosis assessment. Such deep learning methods directly estimate the Cobb angle without providing structural information of the spine which can be used for diagnosis. Although conventional segmentation-based methods can provide the spine structure, they still have limitations in the accurate measurement of the Cobb angle. It would be desirable to build a clinician-friendly diagnostic system for scoliosis that provides not only an automated Cobb angle assessment but also local and global structural information of the spine. This paper addresses this need through the development of a hierarchical method which consisting of three major parts. (1) A confidence map is used to selectively localize and identify all vertebrae in an accurate and robust manner, (2) vertebral-tilt field is used to estimate the slope of an individual vertebra, and (3) the Cobb angle is determined by combining the vertebral centroids with the previously obtained vertebral-tilt field. The performance of the proposed method was validated, resulting in circular mean absolute error of 3.51° and symmetric mean absolute percentage error of 7.84% for the Cobb angle.

Original languageEnglish
Article number9085337
Pages (from-to)84618-84630
Number of pages13
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was supported by the Samsung Science & Technology Foundation under Grant SRFC-IT1902-09. The work of Kang Cheol Kim was supported by the National Research Foundation of Korea (NRF) under Grant 2017R1E1A1A03070653.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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