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

Authors
 KANG CHEOL KIM  ;  HYE SUN YUN  ;  SUNGJUN KIM  ;  JIN KEUN SEO 
Citation
 IEEE ACCESS, Vol.8 : 84618-84630, 2020-05 
Journal Title
 IEEE ACCESS 
Issue Date
2020-05
Abstract
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 segmentationbased 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.
Files in This Item:
T202001661.pdf Download
DOI
10.1109/ACCESS.2020.2992081
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungjun(김성준) ORCID logo https://orcid.org/0000-0002-7876-7901
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/176226
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