Automatic recognition of landmarks on digital dental models

Fundamental principle in improving Dental and Orthodontic treatments is the ability to quantitatively assess and cross-compare their outcomes. Such assessments require calculating distances and angles from 3D coordinates of dental landmarks. The costly and repetitive task of hand-labelling dental models hinder studies requiring large sample size to penetrate statistical noise. We have developed techniques and a software implementing these techniques to map out automatically, 3D dental scans. This process is divided into consecutive steps – determining a model's orientation, separating and identifying the individual tooth and finding landmarks on each tooth – described in this paper. The examples to demonstrate the techniques, software and discussions on remaining issues are provided as well. The software is originally designed to automate Modified Huddard Bodemham (MHB) landmarking for assessing cleft lip/palate patients. Currently only MHB landmarks are supported, however it is extendable to any predetermined landmarks. The software, coupled with intra-oral scanning innovation, should supersede the arduous and error prone plaster model and calipers approach to Dental research, and provide a stepping-stone towards automation of routine clinical assessments such as “index of orthodontic treatment need” (IOTN). [Display omitted] •The first fully automatic detection of landmarks for the time-consuming but objective Modified Huddard Bodenham system.•This method features finding a model's orientation, its peak points, partitioning and identify each tooth.•The methods and software are evaluated on 239 dental models giving 79.7% per-tooth accuracy.•The method may be extended to any predetermined landmarks for automation of routine clinical assessments (e.g. IOTN).