A method for x-ray image landmarks localization using cyclic coordinate-guided strategy

In this study, we present a novel method for pinpointing landmarks in X-ray images, which simultaneously offers computational efficiency and localization precision. Our method leverages a cyclic coordinate-guided strategy that requires fewer model parameters and lower computational costs than traditional heatmap-based supervised methods. This is crucial for medical imaging applications where imaging devices often have limited computational resources yet require high-precision landmark localization. Our methodology involves a two-stage process that employs cyclic inference to optimize landmark localization. In the first stage, non-uniform sampling is used to capture the multiscale features of landmarks. This is followed by a second stage in which cyclic training fine-tunes the landmark coordinates towards their optimal positions. Our results indicate that our two-stage process achieves competitive localization performance with state-of-the-art methods yet with added benefits of lower computational overhead and smaller parameter count. Additionally, a global block was developed to capture global position information of landmarks, and experiments showed its effectiveness and its contribution in enhancing the model's landmark localization accuracy. We validated our method using two publicly available datasets, and the source code for our experiments is available on GitHub: https://github.com/switch626/CCG-CL.git.