MSCANet: A Multi-Scale Separable Convolution Attention Network for Cephalometric Landmark Detection in X-Ray Images

Cephalometric landmark detection in lateral X-rays plays a crucial role in orthodontic treatment, craniofacial growth research, and surgical planning. Traditional manual annotation methods are time-consuming, labor-intensive, and prone to errors. Aiming at these problems, we propose MSCANet, an advanced encoder-decoder framework that leverages multi-scale separable convolution and attention mechanisms to enhance the precision and robustness of cephalometric landmark detection. Specifically, MSCANet integrates three key components: the Multi-Scale Separable Convolution Block (MSCB) for extracting multi-scale features without increasing the parameter count, the Pyramid Skip Feature Fusion (PSFF) module for integrating global features of different sizes with the local feature, and the Enhanced Squeeze-and-Excitation (ESE) module to recalibrate feature maps and emphasize relevant spatial features. These components enable MSCANet to accurately capture both global and local structural details, improving landmark localization in complex scenarios. Experimental results on the public ISBI 2015 dataset show that MSCANet outperforms state-of-the-art methods, achieving significant reductions in mean radial error (MRE) and improved success detection rates (SDR). These results demonstrate the effectiveness of MSCANet in addressing the limitations of existing approaches and underscore the potential of deep learning techniques for automated medical image analysis tasks, particularly in cephalometric X-rays.