  3D anatomical landmarks play an important role in health research. Their
automated prediction/localization thus becomes a vital task. In this paper, we
introduce a deformation method for 3D anatomical landmarks prediction. It
utilizes a source model with anatomical landmarks which are annotated by
clinicians, and deforms this model non-rigidly to match the target model. Two
constraints are introduced in the optimization, which are responsible for
alignment and smoothness, respectively. Experiments are performed on our
dataset and the results demonstrate the robustness of our method, and show that
it yields better performance than the state-of-the-art techniques in most
cases.
