Marking anatomical landmarks in cephalometric radiography is a critical
operation in cephalometric analysis. Automatically and accurately locating
these landmarks is a challenging issue because different landmarks require
different levels of resolution and semantics. Based on this observation, we
propose a novel attentive feature pyramid fusion module (AFPF) to explicitly
shape high-resolution and semantically enhanced fusion features to achieve
significantly higher accuracy than existing deep learning-based methods. We
also combine heat maps and offset maps to perform pixel-wise regression-voting
to improve detection accuracy. By incorporating the AFPF and regression-voting,
we develop an end-to-end deep learning framework that improves detection
accuracy by 7%~11% for all the evaluation metrics over the state-of-the-art
method. We present ablation studies to give more insights into different
components of our method and demonstrate its generalization capability and
stability for unseen data from diverse devices.

本文通过提出基于注意力机制、特征金字塔融合和逐像素回归投票等的深度学习框架，在头影测量图像的解剖标志点自动定位方面显著提高了检测精度，并展示了其在不同设备上的泛化能力和稳定性。