CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection
This provides a practical solution for orthodontic diagnosis by improving accuracy and efficiency in landmark detection, though it is incremental as it builds on existing network architectures.
The paper tackled the problem of automated cephalometric landmark detection from 2D lateral skull X-rays, achieving a mean radial error of 1.23 mm and a success detection rate of 85.5% at 2.0 mm, outperforming prior models with fewer parameters.
Accurate localization of cephalometric landmarks from 2D lateral skull X-rays is vital for orthodontic diagnosis and treatment. Manual annotation is time-consuming and error-prone, whereas automated approaches often struggle with low contrast and anatomical complexity. This paper introduces CephRes-MHNet, a multi-head residual convolutional network for robust and efficient cephalometric landmark detection. The architecture integrates residual encoding, dual-attention mechanisms, and multi-head decoders to enhance contextual reasoning and anatomical precision. Trained on the Aariz Cephalometric dataset of 1,000 radiographs, CephRes-MHNet achieved a mean radial error (MRE) of 1.23 mm and a success detection rate (SDR) @ 2.0 mm of 85.5%, outperforming all evaluated models. In particular, it exceeded the strongest baseline, the attention-driven AFPF-Net (MRE = 1.25 mm, SDR @ 2.0 mm = 84.1%), while using less than 25% of its parameters. These results demonstrate that CephRes-MHNet attains state-of-the-art accuracy through architectural efficiency, providing a practical solution for real-world orthodontic analysis.