Abstract
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 resolutions 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.
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Acknowledgment
This work is supported partially by Innovative Technology Fund (ITS/411/17FX) and General Research Fund (17210419), Hong Kong SAR.
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Chen, R., Ma, Y., Chen, N., Lee, D., Wang, W. (2019). Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_97
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DOI: https://doi.org/10.1007/978-3-030-32248-9_97
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