New Method of Detecting Calcification Regions in Dental Panoramic Radiographs Based on U-PraNet

Abstract

Calcification regions are sometimes observed on dental panoramic radiographs and it is known that these regions are a sign of vascular disease. It has been pointed out that the detection of calcification regions in dental panoramic radiographs can encourage patients to undergo medical checkups by a physician. Medical checkups can prevent the sudden onset of vascular disease. For this purpose, a method of automatically detecting calcification regions using an object detector based on deep learning has been proposed. Although this method significantly reduces the number of false positives compared with conventional methods based on image features, its detection accuracy is still insufficient. In this paper, we propose a method of detecting calcification regions in dental panoramic radiographs using a novel object detector based on deep learning. On the basis of PraNet, which has been increasingly applied to medical image processing in recent years, we introduce the Double U-Net structure and enhance the prediction accuracy for the initial guidance region. The proposed method can improve the detection accuracy for calcification regions. The experimental results show that the proposed method improves the detection performance compared with other methods.