Dense representative tooth landmark/axis detection network on 3D model.

Artificial intelligence (AI) technology is increasingly used for digital orthodontics, but one of the challenges is to automatically and accurately detect tooth landmarks and axes. While many attempts have been made to streamline the orthodontic treatments by automatic technologies, the detection of the tooth landmarks and axes is still left to experienced dental practitioners and performed manually. This is partly because of sophisticated geometric definitions of them, and partly due to large variations among individual tooth and across different types of tooth. As such, we propose a deep learning approach with a labeled dataset by professional dentists to the tooth landmark/axis detection on tooth model that are crucial for orthodontic treatments. Our method can extract not only tooth landmarks in the form of points (e.g. cusps), but also axes that measure the tooth angulation and inclination. The proposed network takes as input a 3D tooth model and predicts various types of the tooth landmarks and axes. Specifically, we encode the landmarks and axes as dense fields defined on the surface of the tooth model. This design choice and a set of added components make the proposed network more suitable for extracting sparse landmarks from a given 3D tooth model. Extensive evaluation of the proposed method was conducted on a set of dental models prepared by experienced dentists. Results show that our method can produce tooth landmarks with high accuracy. Our method was examined and justified via comparison with the state-of-the-art methods as well as the ablation studies. • A novel framework is presented for robustly detecting tooth landmarks and axes from a 3D tooth model. • A new mechanism is proposed for encoding sparse geometric entities (i.e. points and axes) into dense representation. • A novel feature aggregation module is proposed to aggregate features in local and non-local spaces. [ABSTRACT FROM AUTHOR]