用轻量网络精准定位头颅X光片关键点，提升正畸诊断效率。
CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection
- 多头残差结构结合双注意力机制，增强解剖细节识别能力。
- 在1000张影像上实现1.23毫米均方误差，2毫米内定位成功率85.5%。
- 参数量不到最强基线的四分之一，适合临床部署。
从二维侧位头颅X光片中准确定位骨骼标志点对正畸诊断至关重要。人工标注耗时且易出错，而现有自动方法常受低对比度和复杂解剖结构影响。本文提出CephRes-MHNet，一种用于鲁棒高效标志点检测的多头残差卷积网络。该架构融合残差编码、双注意力机制与多头解码器，提升上下文推理与解剖精度。在包含1000张影像的Aariz头颅数据集上训练，模型实现1.23毫米均径误差（MRE）与2.0毫米阈值下85.5%的成功检测率（SDR），优于所有对比模型。尤其超越最强基线AFPF-Net（MRE=1.25 mm，[email protected] mm=84.1%），且参数量不足其25%。结果表明，该模型以架构效率达成顶尖精度，为真实临床正畸分析提供可行方案。
原文摘要 · Abstract (English)
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.
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