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Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs

DC FieldValueLanguage
dc.contributor.author한상선-
dc.contributor.author이채나-
dc.date.accessioned2020-09-29T01:10:59Z-
dc.date.available2020-09-29T01:10:59Z-
dc.date.issued2020-06-
dc.identifier.issn2212-4403-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/179426-
dc.description.abstractObjectives: To evaluate a fully deep learning mask region-based convolutional neural network (R-CNN) method for automated tooth segmentation using individual annotation of panoramic radiographs. Study design: In total, 846 images with tooth annotations from 30 panoramic radiographs were used for training, and 20 panoramic images as the validation and test sets. An oral radiologist manually performed individual tooth annotation on the panoramic radiographs to generate the ground truth of each tooth structure. We used the augmentation technique to reduce overfitting and obtained 1024 training samples from 846 original data points. A fully deep learning method using the mask R-CNN model was implemented through a fine-tuning process to detect and localize the tooth structures. For performance evaluation, the F1 score, mean intersection over union (IoU), and visual analysis were utilized. Results: The proposed method produced an F1 score of 0.875 (precision: 0.858, recall: 0.893) and a mean IoU of 0.877. A visual evaluation of the segmentation method showed a close resemblance to the ground truth. Conclusions: The method achieved high performance for automation of tooth segmentation on dental panoramic images. The proposed method might be applied in the first step of diagnosis automation and in forensic identification, which involves similar segmentation tasks.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAutomation-
dc.subject.MESHImage Processing, Computer-Assisted-
dc.subject.MESHNeural Networks, Computer*-
dc.subject.MESHRadiography, Panoramic-
dc.subject.MESHTooth*-
dc.titleApplication of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs-
dc.typeArticle-
dc.contributor.collegeCollege of Dentistry (치과대학)-
dc.contributor.departmentDept. of Oral and Maxillofacial Radiology (영상치의학교실)-
dc.contributor.googleauthorJeong-Hee Lee-
dc.contributor.googleauthorSang-Sun Han-
dc.contributor.googleauthorYoung Hyun Kim-
dc.contributor.googleauthorChena Lee-
dc.contributor.googleauthorInhyeok Kim-
dc.identifier.doi10.1016/j.oooo.2019.11.007-
dc.contributor.localIdA04283-
dc.contributor.localIdA05388-
dc.contributor.localIdA05388-
dc.relation.journalcodeJ02442-
dc.identifier.pmid31992524-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S2212440319315810-
dc.contributor.alternativeNameHan, Sang Sun-
dc.contributor.affiliatedAuthor한상선-
dc.contributor.affiliatedAuthor이채나-
dc.contributor.affiliatedAuthor이채나-
dc.citation.volume129-
dc.citation.number6-
dc.citation.startPage635-
dc.citation.endPage642-
dc.identifier.bibliographicCitationORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY, Vol.129(6) : 635-642, 2020-06-
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers

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