Detection of Spine Curve and Vertebral Level on Ultrasound Images Using DETR

Convolutional Neural Network (CNN) based method has been shown as a promising tool for automatic curve assessment of scoliosis without time-consuming manual measurement. However, CNN-based method was not reliable to accurately identify vertebral levels. The objective of this study is to investigate the performance of DEtection with TRansformer (DETR) for lamina detection, spine curve measurement and vertebral level identification. The implementation process included the following three steps: 1) automatic detection of lamina pairs based on deep learning methods; 2) assessment of the spinal curvature; 3) identifying the vertebral levels. Total 254 ultrasound images obtained from scoliotic patients were used for the training and evaluation of the proposed method. The average precision (AP) of lamina pairs detection using DETR and Faster R-CNN were 86.12 and 82.03, respectively. Compared to Faster R-CNN, DETR showed smaller MAD (4.47°±3.71° vs 5.3°±4.77°) and higher correlation (0.90 vs 0.85), especially the MAD of DETR was less than the clinical acceptable error (5°). For vertebral level identification, the mean accuracy rate using DETR was increased by 2.8% than Faster R-CNN, while the mean error rate and redundancy rate was reduced by 62% and 71%. The results demonstrated that the transformer-based detection network could achieve more reliable performance on scoliotic curve assessment and vertebral level identification.