Automated detection of cephalometric landmarks using deep neural patchworks

This study evaluated the accuracy of deep neural patchworks (DNPs), a deep learning-based segmentation framework, for automated identification of 60 cephalometric landmarks (bone-, soft tissue- and tooth-landmarks) on CT scans. The aim was to determine whether DNP could be used for routine three-dimensional cephalometric analysis in diagnostics and treatment planning in orthognathic surgery and orthodontics.

Full skull CT scans of 30 adult patients (18 female, 12 male, mean age 35.6 years) were randomly divided into a training and test data set (each n = 15). Clinician A annotated 60 landmarks in all 30 CT scans. Clinician B annotated 60 landmarks in the test data set only. The DNP was trained using spherical segmentations of the adjacent tissue for each landmark. Automated landmark predictions in the separate test data set were created by calculating the center of mass of the predictions. The accuracy of the method was evaluated by comparing these annotations to the manual annotations.

The DNP was successfully trained to identify all 60 landmarks. The mean error of our method was 1.94 mm (SD 1.45 mm) compared to a mean error of 1.32 mm (SD 1.08 mm) for manual annotations. The minimum error was found for landmarks ANS 1.11 mm, SN 1.2 mm, and CP_R 1.25 mm.

The DNP-algorithm was able to accurately identify cephalometric landmarks with mean errors <2 mm. This method could improve the workflow of cephalometric analysis in orthodontics and orthognathic surgery. Low training requirements while still accomplishing high precision make this method particularly promising for clinical use.