Automatic landmarking as a convenient prerequisite for geometric morphometrics. Validation on cone beam computed...

Manual landmarking is used in several manual and semi-automated prediction guidelines for approximation of the nose. The manual placement of landmarks may, however, render the analysis less repeatable due to observer subjectivity and, consequently, have an impact on the accuracy of the human facial approximation. In order to address this subjectivity and thereby improve facial approximations, we are developing an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using non-rigid surface registration. The aim of this study was to validate the automatic landmarking method by comparing the intra-observer errors (INTRA-OE) and inter-observer errors (INTER-OE) between automatic and manual landmarking. Cone beam computed tomography (CBCT) scans of adult South Africans were selected from the Oral and Dental Hospital, University of Pretoria, South Africa. In this study, the validation of the automatic landmarking was performed on 20 3D surfaces. INTRA-OE and INTER-OE were analyzed by registering 41 craniometric landmarks from 10 hard-tissue surfaces and 21 capulometric landmarks from 10 soft-tissue surfaces of the same individuals. Absolute precision of the landmark positioning (both on the samples as well as the template) was assessed by calculating the measurement error (ME) for each landmark over different observers. Systematic error (bias) and relative random error (precision) was further quantified through repeated measures ANOVA (ANOVA-RM). The analysis showed that the random component of the ME in landmark positioning between the automatic observations were on average on par with the manual observations, except for the soft-tissue landmarks where automatic landmarking showed lower ME compared to manual landmarking. No bias was observed within the craniometric landmarking methods, but some bias was observed for capulometric landmarking. In conclusion, this research provides a first validation of the precision and accuracy of the automatic placement of landmarks on 3D hard- and soft-tissue surfaces and demonstrates its utilization as a convenient prerequisite for geometric morphometrics based shape analysis of the nasal complex.