Anatomical Landmarks Annotation on 2D Lateral Cephalograms with Channel Attention

Cephalometric tracing is widely used in orthodontic diagnosis and treatment planning. Since manual landmark localization suffers from severe inter-observer and intra-observer inconsistency, a large number of efforts have been made by researchers to develop automatic localization methods. However, most of the existing methods are developed based on rules which sample uniformly from origin images rather than with the highest density in a focal point and ignore intermediate layers' results in their networks or their outputs' channels. To address the issue, this paper proposes a deep learning model based on multi-scale and multi-channel attention to identify landmarks. The channel attention network is first trained by multi-scale image patches cropped from 100 Cephalograms, and then enhanced by cross-layer connections to extract high-level features, finally involved in the collaboration with a three-layer MLP module to accurately locate coordinates. We conduct extensive evaluation on a real cephalometric X-ray data-set and a Non-public dataset, both achieve promising performance improvements especially in terms of high-precision detection.