Search strategies for multiple landmark detection by submodular maximization

A fundamental issue in multiple landmark detection is the reduction of computational cost. This problem has previously been addressed mainly by reducing the complexity of each individual landmark detector. We address the problem by optimizing the search strategy of multiple landmarks. When the relative positions of landmarks are constrained, the search space can be reduced, thereby reducing the computation. The proposed method leverages the theory of submodular functions to provide a constant factor approximation guarantee of the optimal speed. Although the theory of submodular functions is well known, to the best of our knowledge, this is the first time it is applied to the landmark detection problem. We demonstrate our method by fast and accurate detection of human body landmarks including bones, organs, and vessels in 3D CT images from a diverse dataset of around 2000 volumes with pathological patients. We further provide different search space criteria and variations.