Automatic Assessment of Ultrasound Curvature Angle for Scoliosis Detection Using 3-D Ultrasound Volume Projection Imaging

被引:6
作者
Banerjee, Sunetra [1 ]
Huang, Zixun [2 ]
Lyu, Juan [3 ]
Leung, Frank H. F. [2 ]
Lee, Timothy [4 ]
Yang, De [4 ]
Zheng, Yongping [4 ]
McAviney, Jeb [5 ]
Ling, Sai Ho [1 ,6 ]
机构
[1] Univ Technol Sydney, Sch Elect & Data Engn, Sydney, NSW, Australia
[2] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Hong Kong, Peoples R China
[3] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin, Peoples R China
[4] Hong Kong Polytech Univ, Dept Biomed Engn, Hong Kong, Peoples R China
[5] ScoliCare Clin Sydney South, Kogarah, NSW 2217, Australia
[6] Univ Technol Sydney, CB11 08-215, Ultimo, NSW 2007, Australia
关键词
Bony feature; Cobb angle; Segmentation; Scoliosis; Ultrasound curvature angle; ADOLESCENT IDIOPATHIC SCOLIOSIS; RELIABILITY; VALIDITY; ACCURACY; AID;
D O I
10.1016/j.ultrasmedbio.2023.12.015
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Objective: Scoliosis is a spinal deformation in which the spine takes a lateral curvature, generating an angle in the coronal plane. The conventional method for detecting scoliosis is measurement of the Cobb angle in spine images obtained by anterior X-ray scanning. Ultrasound imaging of the spine is found to be less ionising than traditional radiographic modalities. For posterior ultrasound scanning, alternate indices of the spinous process angle (SPA) and ultrasound curve angle (UCA) were developed and have proven comparable to those of the traditional Cobb angle. In SPA, the measurements are made using the spinous processes as an anatomical reference, leading to an underestimation of the traditionally used Cobb angles. Alternatively, in UCA, more lateral features of the spine are employed for measurement of the main thoracic and thoracolumbar angles; however, clear identification of bony features is required. The current practice of UCA angle measurement is manual. This research attempts to automate the process so that the errors related to human intervention can be avoided and the scalability of ultrasound scoliosis diagnosis can be improved. The key objective is to develop an automatic scoliosis diagnosis system using 3-D ultrasound imaging. Methods: The novel diagnosis system is a three -step process: (i) finding the ultrasound spine image with the most visible lateral features using the convolutional RankNet algorithm; (ii) segmenting the bony features from the noisy ultrasound images using joint spine segmentation and noise removal; and (iii) calculating the UCA automatically using a newly developed centroid pairing and inscribed rectangle slope method. Results: The proposed method was evaluated on 109 patients with scoliosis of different severity. The results obtained had a good correlation with manually measured UCAs (R2 = 0.9784 for the main thoracic angle and R2 = 0.9671 for the thoracolumbar angle) and a clinically acceptable mean absolute difference of the main thoracic angle (2.82 +/- 2.67 degrees) and thoracolumbar angle (3.34 +/- 2.83 degrees). Conclusion: The proposed method establishes a very promising approach for enabling the applications of economic 3-D ultrasound volume projection imaging for mass screening of scoliosis.
引用
收藏
页码:647 / 660
页数:14
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