Accurate localization of cephalometric landmarks from 2D lateral skull X-rays is vital for orthodontic diagnosis and treatment. Manual annotation is time-consuming and error-prone, whereas automated approaches often struggle with low contrast and anatomical complexity. This paper introduces CephRes-MHNet, a multi-head residual convolutional network for robust and efficient cephalometric landmark detection. The architecture integrates residual encoding, dual-attention mechanisms, and multi-head decoders to enhance contextual reasoning and anatomical precision. Trained on the Aariz Cephalometric dataset of 1,000 radiographs, CephRes-MHNet achieved a mean radial error (MRE) of 1.23 mm and a success detection rate (SDR) @ 2.0 mm of 85.5%, outperforming all evaluated models. In particular, it exceeded the strongest baseline, the attention-driven AFPF-Net (MRE = 1.25 mm, SDR @ 2.0 mm = 84.1%), while using less than 25% of its parameters. These results demonstrate that CephRes-MHNet attains state-of-the-art accuracy through architectural efficiency, providing a practical solution for real-world orthodontic analysis.

翻译：从二维侧位头颅X光片中精确定位头影测量标志点对于正畸诊断与治疗至关重要。手动标注耗时且易出错，而自动化方法常因低对比度与解剖结构复杂性而受限。本文提出CephRes-MHNet，一种用于稳健高效头影测量标志点检测的多头残差卷积网络。该架构融合残差编码、双重注意力机制与多头解码器，以增强上下文推理与解剖学精度。在包含1000张放射影像的Aariz头影测量数据集上训练后，CephRes-MHNet实现了1.23毫米的平均径向误差（MRE）及85.5%的2.0毫米阈值成功检测率（SDR），性能超越所有评估模型。特别地，其表现优于最强基线——注意力驱动的AFPF-Net（MRE = 1.25毫米，SDR @ 2.0毫米 = 84.1%），且参数量不足后者的25%。这些结果表明，CephRes-MHNet通过架构效率实现了最先进的精度，为实际正畸分析提供了实用解决方案。