An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

  • Zhusi Zhong
  • Jie Li
  • Zhenxi Zhang
  • Zhicheng Jiao
  • Xinbo GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder model for landmark detection, which combines global landmark configuration with local high-resolution feature responses. The proposed framework is based on a 2-stage u-net, regressing the multi-channel heatmaps for landmark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, an Expansive Exploration strategy is applied to improve robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated the proposed framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, the proposed framework achieves state-of-the-art results.


Landmark detection Deep learning Heatmap regression Attention mechanism 2D X-ray cephalometric analysis 



This work was supported in part by the National Natural Science Foundation of China under Grant 61671339, 61432014 and 61772402, and in part by National High-Level Talents Special Support Program of China under Grant CS31117200001.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhusi Zhong
    • 1
  • Jie Li
    • 1
  • Zhenxi Zhang
    • 1
  • Zhicheng Jiao
    • 2
  • Xinbo Gao
    • 1
    Email author
  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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