Date: December 2020.
Source: European Journal of Orthodontics, cjaa069,
Introduction: Due to technological advances, the quantification of facial form can now be done via three-dimensional (3D) photographic systems such as stereophotogrammetry. To enable comparison with traditional cephalometry, soft-tissue anatomical landmark definitions have been modified to incorporate the third dimension. Annotating these landmarks manually, however, is still a time-consuming and arduous process.
Objective: To develop an automated algorithm to accurately identify anatomical landmarks on three-dimensional soft tissue images.
Methods: Thirty 3dMD images were selected from a private orthodontic practice consisting of 15 males and 15 females between 9 and 17 years of age. The soft-tissue 3D images were aligned along a reference plane to setup a Cartesian coordinate system. Screened by 2 observers, 21 landmarks were manually annotated and their coordinates defined. An automated landmark identification algorithm, based on their anatomical definitions, was developed to compare the landmark validity against the manually identified counterpart.
Results: Twenty-one landmarks were analysed in detail. Inter-observer and intra-observer reliability using ICC was >0.9. The average difference and standard deviation between manual and automated methods for all landmarks was 3.2 and 1.64 mm, respectively. Sixteen out of twenty-one landmarks had a mean difference less than 4 mm. The landmarks of greatest agreement (≤2 mm) were mainly in the midline: pronasale, subnasale, subspinale, labiale superius, stomion, with the exception of chelion right. Five linear facial measurements were found to have moderate to good agreement between the manual and automated identification methods.
Conclusions: The developed algorithm was determined to be clinically relevant in the detection of midsagittal landmarks and associated measurements within the studied sample of adolescent Caucasian subjects.

Article: Accuracy of an automated method of 3D soft tissue landmark detection.
Authors: Sanjana Baksi, Simon Freezer, Takeshi Matsumoto, Craig Dreyer.