Learnable Skeleton-Based Medical Landmark Estimation with Graph Sparsity and Fiedler Regularizations

Recent development in heatmap regression-based models have been central to anatomical landmark detection, yet their efficiency is often limited due to the lack of skeletal structure constraints. Despite the notable use of graph convolution networks (GCNs) in human pose estimation and facial landmark detection, manual construction of skeletal structures remains prevalent, presenting challenges in medical contexts with numerous non-intuitive structure. This paper introduces an innovative skeleton construction model for GCNs, integrating graph sparsity and Fiedler regularization, diverging from traditional manual methods. We provide both theoretical validation and a practical implementation of our model, demonstrating its real-world efficacy. Additionally, we have developed two new medical datasets tailored for this research, along with testing on an open dataset. Our results consistently show our method’s superior performance and versatility in anatomical landmark detection, establishing a new benchmark in the field, as evidenced by extensive testing across diverse datasets.