  Cephalometric landmark detection on lateral skull X-ray images plays a
crucial role in the diagnosis of certain dental diseases. Accurate and
effective identification of these landmarks presents a significant challenge.
Based on extensive data observations and quantitative analyses, we discovered
that visual features from different receptive fields affect the detection
accuracy of various landmarks differently. As a result, we employed an image
pyramid structure, integrating multiple resolutions as input to train a series
of models with different receptive fields, aiming to achieve the optimal
feature combination for each landmark. Moreover, we applied several data
augmentation techniques during training to enhance the model's robustness
across various devices and measurement alternatives. We implemented this method
in the Cephalometric Landmark Detection in Lateral X-ray Images 2023 Challenge
and achieved a Mean Radial Error (MRE) of 1.62 mm and a Success Detection Rate
(SDR) 2.0mm of 74.18% in the final testing phase.
