Automated Cephalometric Analysis on Lateral Headplates for Orthodontic Diagnosis

Cephalometric analysis is the process of placing anatomical landmark points on lateral headplate X-ray scans. Placing these points with precision is crucial as the angle and distancemeasurements required during treatment are calculated using these points. This project aimed to automate the process of cephalometric analysis on lateral headplates X-ray scans. An object detection model followed by a heatmap regression was proposed to find the landmark locations. In total, 1000 images were acquired fromthe Radboud UMC, Department of Dentistry. Experiments with different network architecture, bounding box sizes, anchor optimisation methods, augmentations, and heatmap regression strategies were performed to converge the model resulting in low loss and high average precision and recall score. The best performing model was Cascade RCNN with a pre-trained ResNet101 as the backbone, without the additional heatmap regressor unit. The final network achieved a mean radial error (MRE) of 1.12 with a standard deviation (SD) of 0.81 and success detection rate(SDR) of 85.76%. The average precision with intersection over union (IOU) ranging 0.5:0.9 and step size increase of 0.05 was 0.857. The automated cephalometric analysis model will save time, provide objective results, and help the orthodontists and surgeons in initial screening and mid-treatment assessment.