摘要： 脊柱侧凸的诊断依赖Cobb角的精确测量，而传统手工测量Cobb角存在主观性强、效率低及一致性差的问题，难以满足临床标准化与高效性需求。本研究提出了一种基于几何约束混合注意力 SwinUNet的脊柱Cobb角自动测量方法（GHA-SwinUNet）。该方法以U-Net为基础架构，引入Swin Transformer模块增强全局结构建模能力，结合混合局部通道注意力（MLCA）提升椎体局部细节感知，并设计几何约束后处理策略解决椎体黏连问题；同时，在Cobb角计算阶段，采用端板直线拟合法规避传统中点法的几何偏差。实验结果表明，该方法在自建脊柱X光数据集上分割性能优异：Dice相似系数（DSC）达0.9483、精确率（Precision）为0.9504、平均交并比（mIoU）为0.9483，较传统U-Net的DSC提升1.11%、较 MA-Net的DSC提升0.27%。同时，在Synapse和AASCE2019公开数据集的跨验证中，模型保持稳定性能（DSC分别为 0.9512、0.9425）。Cobb角自动测量与人工测量的一致性相关系数（ICC）大于0.90，平均绝对偏差（MAD）约为 3°，表明一致性良好。综上，该方法在保证分割与测量精度的同时兼顾效率，且在多源影像上泛化能力强，为脊柱侧凸的定量化评估与临床辅助诊断提供了可靠技术支撑。

Abstract: The diagnosis of scoliosis relies on the precise measurement of the Cobb Angle. Traditional manual measurement has problems such as strong subjectivity, low efficiency and poor consistency, which is difficult to meet the clinical standardization and efficiency requirements. To solve this problem, this study proposes an automatic measurement method for spinal Cobb angles based on geometric constraint hybrid attention SwinUNet (GHA-SwinUNet). This method is based on the U-Net architecture, introduces the Swin Transformer module to enhance the ability of global structural modeling, combines the Hybrid Local Channel Attention (MLCA) to improve the perception of local details of the vertebral body, and designs a geometric constraint post-processing strategy to solve the problem of vertebral adhesion. In the Cobb Angle calculation stage, the end plate straight line fitting method is adopted to avoid the geometric deviation of the traditional midpoint method. The experimental results show that this method has excellent segmentation performance on the self-built spinal X-ray dataset: The Dice similarity coefficient (DSC) reached 0.9483, the Precision was 0.9504, and the average cross-union ratio (mIoU) was 0.9483, which was 1.11% higher than that of the traditional U-Net DSC and 0.27% higher than that of the MA-Net DSC. Meanwhile, in the cross-validation of the Synapse and AASCE2019 public datasets, the model maintained stable performance (DSC values were 0.9512 and 0.9425, respectively). The consistency correlation coefficient (ICC) between the automatic measurement and manual measurement of Cobb angles is greater than 0.90, and the mean absolute deviation (MAD) is approximately 3°, indicating good consistency. In conclusion, this method not only ensures the accuracy of segmentation and measurement but also takes into account efficiency. Moreover, it has strong generalization ability of multi-source images, providing reliable technical support for the quantitative assessment and clinical auxiliary diagnosis of scoliosis.