Automated Segmentation and Diagnostic Measurement for the Evaluation of Cervical Spine Injuries Using X-Rays

AbstractAccurate assessment of cervical spine X-ray images through diagnostic metrics plays a crucial role in determining appropriate treatment strategies for cervical injuries and evaluating surgical outcomes. Such assessment can be facilitated through the use of automatic methods such as machine learning and computer vision algorithms. A total of 852 cervical X-rays obtained from Gachon Medical Center were used for multiclass segmentation of the craniofacial bones (hard palate, basion, opisthion) and cervical spine (C1 –C7), incorporating architectures such as EfficientNetB4, DenseNet201, and InceptionResNetV2. Diagnostic metrics automatically measured using computer vision algorithms were compared with manually measured metrics through Pearson’s correlation coefficient and pairedt-tests. The three models demonstrated high average dice coefficient values for the cervical spine (C1, 0.93; C2, 0.96; C3, 0.96; C4, 0.96; C5, 0.96; C6, 0.96; C7, 0.95) and lower values for the craniofacial bones (hard palate, 0.69; basion, 0.81; opisthion, 0.71). Comparison of manually measured metrics and automatically measured metrics showed high Pearson ’s correlation coefficients in McGregor’s line (r = 0.89), space available cord (r = 0.94), cervical sagittal vertical axis (r = 0.99), cervical lordosis (r = 0.88), lower correlations in basion-dens interval (r = 0.65), basion-axial interval (r = 0.72), and Powers ratio (r = 0.62). No metric showed adjusted ...
Source: Journal of Digital Imaging - Category: Radiology Source Type: research