Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting
This work addresses the challenge of accurate landmark detection in medical imaging for cephalometric analysis, representing an incremental advancement with specific gains.
The paper tackled the problem of automatically detecting anatomical landmarks in cephalometric radiography by proposing an attentive feature pyramid fusion module and regression-voting method, resulting in a 7% to 11% improvement in detection accuracy over state-of-the-art methods.
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.