Cephalometric landmark detection on lateral skull X-ray images plays a crucial role in the diagnosis of certain dental diseases. Accurate and effective identification of these landmarks presents a significant challenge. Based on extensive data observations and quantitative analyses, we discovered that visual features from different receptive fields affect the detection accuracy of various landmarks differently. As a result, we employed an image pyramid structure, integrating multiple resolutions as input to train a series of models with different receptive fields, aiming to achieve the optimal feature combination for each landmark. Moreover, we applied several data augmentation techniques during training to enhance the model's robustness across various devices and measurement alternatives. We implemented this method in the Cephalometric Landmark Detection in Lateral X-ray Images 2023 Challenge and achieved a Mean Radial Error (MRE) of 1.62 mm and a Success Detection Rate (SDR) 2.0mm of 74.18% in the final testing phase.

针对侧面头颅X射线图片的颅测定点检测在特定牙科疾病诊断中起着关键作用，通过以多个分辨率作为输入整合多种感受野的图像金字塔结构来训练一系列具有不同感受野的模型，其对不同的定点具有不同的检测准确率影响。此外，我们还采用了多种数据增强技术来增强模型在不同设备和测量选择方面的鲁棒性。我们将这种方法应用于2023年侧面头颅X射线图像颅测定点检测挑战中，在最终测试阶段实现了1.62 mm的平均径向误差（MRE）和74.18%的成功检测率（SDR）。

多分辨率融合的全自动颅面测量地标检测