Improvement of Detection Accuracy for Calcification Regions in Dental Panoramic Radiographs Using LVAT

Carotid arteries on dental panoramic radiographs may show areas of calcification, which is a sign of vascular disease. The sudden onset of vascular disease can be prevented by a dentist detecting these areas at a dental clinic and prompting the patient to visit a physician. A method based on semantic segmentation was proposed as an automatic detection method for this purpose. However, because of the small number of available data, the detection accuracy for calcification regions was insufficient. In this study, we propose a method that applies a type of adversarial training called latent space virtual adversarial training (LVAT). We also describe a method to improve the pre-training of a variational autoencoder, which is essential in LVAT, using healthy subjects’ data. We expect this method to improve detection accuracy, even with a limited number of data. We demonstrated the effectiveness of the proposed method by applying it to actual data.