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Cephalometric tracing method is usually used in orthodontic diagnosis andtreatment planning. In this paper, we propose a deep learning based frameworkto automatically detect anatomical landmarks in cephalometric X-ray images. Wetrain the deep encoder-decoder for landmark detection, and combine globallandmark configuration with local high-resolution feature responses. Theproposed frame-work is based on 2-stage u-net, regressing the multi-channelheatmaps for land-mark detection. In this framework, we embed attentionmechanism with global stage heatmaps, guiding the local stage inferring, toregress the local heatmap patches in a high resolution. Besides, the ExpansiveExploration strategy improves robustness while inferring, expanding thesearching scope without increasing model complexity. We have evaluated ourframework in the most widely-used public dataset of landmark detection incephalometric X-ray images. With less computation and manually tuning, ourframework achieves state-of-the-art results.

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