LAUSR

BACKGROUND Scoliosis is a type of spinal deformity, which is harmful to a person's health. In severe cases, it can trigger paralysis or death. The measurement of Cobb angle plays an essential role in assessing the severity of scoliosis. PURPOSE The aim of this paper is to propose an automatic system for landmark detection and Cobb angle estimation, which can effectively help clinicians diagnose and treat scoliosis. METHODS A novel hybrid framework was proposed to measure Cobb angle precisely for clinical diagnosis, which was referred as W-Transformer due to its w-shaped architecture. First, a convolutional neural network of cascade residual blocks as our backbone was designed. Then a transformer was fused to learn the dependency information between spine and landmarks. In addition, a reinforcement branch was designed to improve the overlap of landmarks, and an improved prediction module was proposed to fine-tune the final coordinates of landmarks in Cobb angles estimation. Besides, the public AASCE MICCAI 2019 challenge was served as dataset. It supplies 609 manually labeled spine AP X-ray images, each of which contains a total of 68 landmark labels and three Cobb Angles tags. RESULTS From the perspective of the AASCE MICCAI 2019 challenge, we achieved a lower symmetric mean absolute percentage error (SMAPE) of 8.26% for all Cobb angles and the lowest averaged detection error of 50.89 in terms of landmark detection, compared with many state-of-the-art methods. We also provided the SMAPEs for the Cobb angles of the Proximal-Thoracic (PT), the Main-Thoracic (MT) and the Thoracic-Lumbar (TL) area, which are 5.27%, 14.59% and 20.97% respectively, however, these data were not covered in most previous studies. Statistical analysis demonstrates that our model has obtained a high level of Pearson correlation coefficient of 0.9398 (p<0.001), which shows excellent reliability of our model. Our model can yield 0.9489 (p<0.001), 0.8817 (p<0.001) and 0.9149 (p<0.001$) for PT, MT and TL, respectively. The overall variability of Cobb angle measurement is less than 4°, implying clinical value. And the mean absolute deviation (Standard Deviation) for three regions is 3.64° (4.13°), 3.84° (4.66°) and 3.80° (4.19°). The results of Student paired t-test indicate that no statistically significant differences are observed between manual measurement and our automatic approach (p value is always > 0.05). Regarding the diagnosis of scoliosis (Cobb angle > 10°), the proposed method achieves a high sensitivity of 0.9577 and a specificity of 0.8475 for all spinal regions. CONCLUSIONS This study offers a brand-new automatic approach that is potentially of great benefit of the complex task of landmark detection and Cobb angle evaluation, which can provide helpful navigation information about the early diagnosis of scoliosis. This article is protected by copyright. All rights reserved.