An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
This work addresses a domain-specific problem in medical imaging for orthodontics, offering an incremental improvement in landmark detection efficiency.
The authors tackled automatic anatomical landmark detection in cephalometric X-ray images for orthodontic diagnosis, achieving state-of-the-art results with less computation and manual tuning on a widely-used public dataset.
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 for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark 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, the Expansive Exploration strategy improves robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated our framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, our framework achieves state-of-the-art results.