IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray.

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications.