Automated landmarking for palatal shape analysis using geometric deep learning

OBJECTIVES

To develop and evaluate a geometric deep-learning network to automatically place seven palatal landmarks on digitized maxillary dental casts.

SETTINGS AND SAMPLE POPULATION

The sample comprised individuals with permanent dentition of various ethnicities. The network was trained from manual landmark annotations on 732 dental casts and evaluated on 104 dental casts.

MATERIALS AND METHODS

A geometric deep-learning network was developed to hierarchically learn features from point-clouds representing the 3D surface of each cast. These features predict the locations of seven palatal landmarks.

RESULTS

Repeat-measurement reliability was <0.3 mm for all landmarks on all casts. Accuracy is promising. The proportion of test subjects with errors less than 2 mm was between 0.93 and 0.68, depending on the landmark. Unusually shaped and large palates generate the highest errors. There was no evidence for a difference in mean palatal shape estimated from manual compared to the automatic landmarking. The automatic landmarking reduces sample variation around the mean and reduces measurements of palatal size.

CONCLUSIONS

The automatic landmarking method shows excellent repeatability and promising accuracy, which can streamline patient assessment and research studies. However, landmark indications should be subject to visual quality control.

Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery

BACKGROUND

Manual landmarking is a time consuming and highly professional work. Although some algorithm-based landmarking methods have been proposed, they lack flexibility and may be susceptible to data diversity.

METHODS

The CT images from 66 patients who underwent oral and maxillofacial surgery (OMS) were landmarked manually in MIMICS. Then the CT slices were exported as images for recreating the 3D volume. The coordinate data of landmarks were further processed in Matlab using a principal component analysis (PCA) method. A patch-based deep neural network model with a three-layer convolutional neural network (CNN) was trained to obtain landmarks from CT images.

RESULTS

The evaluating experiment showed that this CNN model could automatically finish landmarking in an average processing time of 37.871 seconds with an average accuracy of 5.785 mm.

CONCLUSION

This study shows a promising potential to relieve the workload of the surgeon and reduces the dependence on human experience for OMS landmarking.

Three-dimensional soft tissue effects of mandibular midline distraction and surgically assisted rapid maxillary expansion: an automatic stereophotogrammetry landmarking analysis

Studies on mandibular midline distraction (MMD) are mostly performed using conventional research methods. Concerning surgically assisted rapid maxillary expansion (SARME), more research is conducted using three-dimensional (3D) techniques. Research on bimaxillary expansion, the combination of MMD and SARME, is reported sparsely. The main objective of this study was to provide a 3D evaluation of soft tissue effects following SARME and/or MMD. Patients who underwent SARME and/or MMD between 2008 and 2013 were included. Stereophotogrammetry was undertaken at the following time points: preoperative (T1), immediately post-distraction (T2), 1year postoperative (T3). An automatic 3D facial landmarking algorithm using two-dimensional Gabor wavelets was applied for the analysis. Twenty patients who had undergone SARME were included, 12 of whom had undergone bimaxillary expansion. Age at the time of surgery ranged from 16 to 47 years. There was a significant downward displacement of soft tissue pogonion. Furthermore, there was a significant mean increase of 2.20mm in inter-alar width and a non-significant mean increase of 1.77mm in inter-alar curvature point width. In conclusion, automatic stereophotogrammetry landmarking analysis of soft tissue effects showed downward displacement of soft tissue pogonion following bimaxillary expansion and transverse widening of the inter-alar width and a tendency towards an increase in inter-alar curvature point width after SARME.