Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection
This work addresses the problem of accurate landmark detection for dental disease diagnosis, representing an incremental improvement in a domain-specific task.
The paper tackled the challenge of fully automatic cephalometric landmark detection on lateral skull X-ray images by using a multi-resolution fusion method, achieving a Mean Radial Error of 1.62 mm and a Success Detection Rate of 74.18% at 2.0 mm in a 2023 challenge.
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.