Statistical shape model for anatomical landmarks prediction in humerus and mixed reality visualization

This work presents an automated pipeline for statistical shape modeling and evaluation of 3D anatomical landmarks in the left and right humeri from Computed Tomography (CT) data. The process consists of training and testing stages, designed to ensure reproducibility, robustness, and anatomical accuracy. The model is designed to reduce bias related to template selection, human selection variability, and differences in laterality between left and right humeri. Anatomical landmarks play a pivotal role in semi- or fully automated implant placement. This model is capable of predicting the landmarks in a fully automated way with the possibility to adjust the results manually, if needed, in a mixed reality environment. The pipeline involves segmentation from 3D medical images (CT scans), manual landmarks selection for training images, meshing, rigid and non-rigid registration, and cross-validation of the model by performing 10 predictions with different template selection for each of the 20 test cases (10 left and 10 right humeri) for a total of 200 predictions. An interobserver variability study is conducted to validate the model, utilizing annotations from 5 operators (for a total number of 50 annotations). This model shows comparable results in comparison with human annotation, having the advantage of being automatic once the model is trained.