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

• Published in: Ultrasound in medicine & biology Vol. 50; no. 5; pp. 647 - 660
• Main Authors: Banerjee, Sunetra, Huang, Zixun, Lyu, Juan, Leung, Frank H.F., Lee, Timothy, Yang, De, Zheng, Yongping, McAviney, Jeb, Ling, Sai Ho
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01-05-2024
• Subjects: Bony feature; Cobb angle; Humans; Imaging, Three-Dimensional; Radiography; Scoliosis; Scoliosis - diagnostic imaging; Segmentation; Spine - diagnostic imaging; Ultrasonography - methods; Ultrasound curvature angle; Ultrasound curvature angle; Bony feature; Segmentation; Scoliosis; Cobb angle
• DOI: 10.1016/j.ultrasmedbio.2023.12.015
• EISSN: 1879-291X
• ISSN: 0301-5629

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.
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.
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 andR2=0.9671 for the thoracolumbar angle) and a clinically acceptable mean absolute difference of the main thoracic angle (2.82 ± 2.67°) and thoracolumbar angle (3.34 ± 2.83°).
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.