在检测侧面脊柱关键点时，由于受到器官遮挡的影响，以往的热图回归方法难以区分不同椎骨上的关键点，容易出现关键点与对应椎骨的匹配错误。为了解决这个问题，提出了一个新的单阶段侧面脊柱关键点检测方法，该方法同时预测关键点热图和关键点匹配线索（椎骨中心热图和关键点offset），利用匹配线索建立关键点与对应椎骨的匹配关系。为了提升匹配效果，提出几何感知特征增强模块，通过提取关键点特征增强椎骨中心的特征表达。此外，利用加权损失函数缓解关键点热图和椎骨中心热图中正负样本比例失衡问题。实验结果表明，所提方法的平均检测误差为8.84，相较于性能第二的方法精度提升36%。

In lateral spine landmark detection, the previous heatmap regression methods have difficulty in distinguishing landmarks on different vertebrae due to the influence of organ occlusion and are prone to landmark and vertebrae matching errors. To solve this problem, we propose a new one-stage lateral spine landmark detection method, which simultaneously predicts the landmark heatmap and landmark matching clue (vertebra center heatmap and landmark offset), and uses the matching clue to match the landmarks with the corresponding vertebra. In order to improve the matching effect, we propose the geometry-aware feature aggregator module, which can extract the landmark features on the vertebra to enhance the feature representation of the vertebra center. We also use a weighted loss function to alleviate the imbalance of positive and negative samples in the landmark and the vertebra center heatmaps. Experimental results show that the average detection error of the proposed method is 8.84, which has 36% improvement in accuracy compared to the method with the second-highest performance.