Detection of the root resorption from panoramic X-ray images using deep metric learning

Kosei Tamura; Tohru Kamiya; Masashi Oda; Tatsurou Tanaka; Yasuhiro Morimoto

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자료유형: 학술대회자료

저자정보: Kosei Tamura (Kyushu Institute of Technology) Tohru Kamiya (Kyushu Institute of Technology) Masashi Oda (Kyushu Dental University) Tatsurou Tanaka (Kyushu Dental University) Yasuhiro Morimoto (Kyushu Dental University)

저널정보: 제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2021

발행연도: 2021.10

수록면: 1,800 - 1,803 (4page)

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Root resorption is a pathological process characterized by the loss of tooth roots because of inflammation induced by bacterial infection, trauma, physical or chemical irritation. As a result, the development of periodontal disease, increased susceptibility to infection and crooked teeth. In the worst case, it can lead to tooth extraction. Root resorption is often caused by pressure during orthodontic treatment. The presence of root resorption should be checked regularly during orthodontic treatment, as it often occurs. It is necessary to check for root resorption periodically during orthodontic treatment. However, it is difficult to detect the root resorption using a panoramic radiograph. As a result, root resorption is often latent and goes undetected. In this paper, we propose an image analysis method based on deep learning technique for detecting the root resorption on panoramic radiograph. We incorporate the EfficientNet for feature extraction in deep learning to the center loss and triplet loss as the loss function for metric learning. Our proposed method performed to 337 images which is obtained by panoramic radiograph. Accuracy of 71%, true positive rate of 77%, false positive rate of 30% were obtained.

#Root resorption #Abnormal detection #EfficientNet #Deep metric learning #Triplet loss #Center loss #X-ray image #Computer aided diagnosis

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