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 multihead 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.