CephaNN: A Multi-Head Attention Network for Cephalometric Landmark Detection

Cephalometric landmark detection is a crucial step in orthodontic and orthognathic treatments. To detect cephalometric landmarks accurately, we propose a novel multi-head attention neural network (CephaNN). CephaNN is an end-to-end network based on the heatmaps of annotated landmarks, and it consists of two parts, the multi-head part and the attention part. In the multi-head part, we adopt multi-head subnets to gain comprehensive knowledge of various subspaces of a cephalogram. The intermediate supervision is applied to accelerate the convergence. Based on the feature maps learned from the multi-head Part, the attention part applies the multi-attention mechanism to obtain a refined detection. For solving the class imbalance problem, we propose a region enhancing (RE) loss, to enhance the efficient regions on the regressed heatmaps. Experiments in the benchmark dataset demonstrate that CephaNN is state-of-the-art with the detection accuracy of 87.61% in the clinically accepted 2.0-mm range. Furthermore, CephaNN is efficient in classifying the anatomical types and robust in a real application on a 75-landmark dataset.