Multistage Probabilistic Approach for the Localization of Cephalometric Landmarks

The accurate and reproducible localization of cephalometric landmarks is an important procedure for treatment planning and clinical practice in orthodontics and maxillofacial surgery. In this paper, we propose a new multistage cephalometric landmark localization method that exploits local appearances and global characteristics simultaneously. To be precise, a convolutional neural network(CNN) is trained by minimizing the sum of all landmark errors. Since landmarks are considered simultaneously, global hard/soft tissue characteristics, as well as landmark relations, can be reflected in this stage. Then, we exploit local appearances by using high-resolution cropped images. In this second stage, we train CNNs for individual landmarks, respectively. Finally, we improve the localization performance of cephalometric landmarks of the mandible with linear estimators. Experiments on ISBI2015 dataset have shown that the proposed method outperforms conventional methods. Also, the proposed method allows us to evaluate confidence (e.g., standard deviational ellipses) due to its probabilistic formulation.