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

Understand

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.