Anatomical Landmark Detection Using a Multiresolution Learning Approach with a Hybrid Transformer-CNN Model

Accurate localization of anatomical landmarks has a critical role in clinical diagnosis, treatment planning, and research. Most existing deep learning methods for anatomical landmark localization rely on heatmap regression-based learning, which generates label representations as 2D Gaussian distributions centered at the labeled coordinates of each of the landmarks and integrates them into a single spatial resolution heatmap. However, the accuracy of this method is limited by the resolution of the heatmap, which restricts its ability to capture finer details. In this study, we introduce a multiresolution heatmap learning strategy that enables the network to capture semantic feature representations precisely using multiresolution heatmaps generated from the feature representations at each resolution independently, resulting in improved localization accuracy. Moreover, we propose a novel network architecture called hybrid transformer-CNN (HTC), which combines the strengths of both CNN and vision transformer models to improve the network's ability to effectively extract both local and global representations. Extensive experiments demonstrated that our approach outperforms state-of-the-art deep learning-based anatomical landmark localization networks on the numerical XCAT 2D projection images and two public X-ray landmark detection benchmark datasets. Our code is available at https://github.com/seriee/Multiresolution-HTC.git.