Deep learningによる口内法X線画像の自動分類
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| Published in | 歯科放射線 Vol. 60; no. 2; pp. 53 - 57 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
特定非営利活動法人 日本歯科放射線学会
2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0389-9705 2185-6311 |
| DOI | 10.11242/dentalradiology.60.53 |
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| Author | 飯田, 幸弘 勝又, 明敏 小日向, 清美 北野, 倫哉 藤田, 広志 西山, 航 森, 瑞穂 |
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| Copyright | 2021 特定非営利活動法人 日本歯科放射線学会 |
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| References | 15. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko BB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacal Radiology. 2019；48（4）：20180051. 4. 勝又明敏，早川吉彦，杉原義人，木下 豪，坂本 博，玉川裕夫，青木孝文，江島堅一郎，新井嘉則．歯科におけるDICOMと歯科医療情報標準化の展開．歯科放射線．2017；56（2）：97-106. 13. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiology. 2019；35（3）：301-307. 12. Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019；128（4）：424-430. 2. 有田正博，西田郁子，吉野賢一，中村恵子，小城辰郎，北村知昭，木尾哲朗，大住伴子，坂本英治，庄野庸雄，黒川英雄，安細敏弘，一田利道，佐藤耕一，篠原雄二，瀬田祐司，園木一男，芳賀健輔，村田貴俊，林田 裕，横田 誠，寺下正道，西原達次．九州歯科大学におけるOSCEトライアルの評価．九州歯科学会雑誌．2004：58（6）；213-222. 8. Sanjeev BK, Ali Al-ehaideb, Prabhadevi CM, Satish VS, Hosam AB, Sachin CS, Shilpa B. Developments, application, and performance of artificial intelligence in dentistry-A systematic review. Journal of Dental Sciences. 2020；published online. 6. DICOM standard CP 1444 Parts 3, 6, 17. Add additional dental view sets to Structured Display. ftp://medical.nema.org/medical/dicom/final/cp1444_ft.pdf 11. Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology. 2019；published online. 5. 日本歯科放射線学会：日本歯科放射線学会規格．口内法撮影X線画像の画像配置，JSOMR X-0001；2015. 10. Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, Funakoshi T, Ariji Y, Fujita H, Katsumata A, Yoshiura K, Ariji E. Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images. Dentomaxillofac Radiology. 2020；49（3）：published online. 16. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific Report. 2019；9（1）：3840. 1. 有地榮一郎，勝又明敏，小林 馨，櫻井 孝，藤田広志，本田和也，編．デジタルデンティストリー 医療情報とデジタル画像超入門．第1版．京都：永末書店；2015：p.54-70 14. Zhang K, Wu KJ, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics. 2018；68：61-70. 3. Iwasaki H, Honda E, Nishitani H, Takahashi H, Yamamoto Y, Ooguro T, Tanaka K. Hanging protocol and viewers for a dental full picture archiving and communication system （PACS）. Dentomaxillofac Radiol. 2007；36（5）：285-295. 9. Muramatsu C, Morishita T, Takahashi R, Hayashi T, Nishiyama W, Ariji Y, Zhou X, Hara T, Katsumata A, Ariji E, Fujita H. Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data. Oral Radiology. 2020；published online. 7. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep Convolutional neural networks. Adv Neural Inf Process Syst. 2012；25：1-9. |
| References_xml | – reference: 6. DICOM standard CP 1444 Parts 3, 6, 17. Add additional dental view sets to Structured Display. ftp://medical.nema.org/medical/dicom/final/cp1444_ft.pdf – reference: 10. Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, Funakoshi T, Ariji Y, Fujita H, Katsumata A, Yoshiura K, Ariji E. Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images. Dentomaxillofac Radiology. 2020；49（3）：published online. – reference: 13. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiology. 2019；35（3）：301-307. – reference: 15. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko BB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacal Radiology. 2019；48（4）：20180051. – reference: 1. 有地榮一郎，勝又明敏，小林 馨，櫻井 孝，藤田広志，本田和也，編．デジタルデンティストリー 医療情報とデジタル画像超入門．第1版．京都：永末書店；2015：p.54-70． – reference: 4. 勝又明敏，早川吉彦，杉原義人，木下 豪，坂本 博，玉川裕夫，青木孝文，江島堅一郎，新井嘉則．歯科におけるDICOMと歯科医療情報標準化の展開．歯科放射線．2017；56（2）：97-106. – reference: 12. Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019；128（4）：424-430. – reference: 3. Iwasaki H, Honda E, Nishitani H, Takahashi H, Yamamoto Y, Ooguro T, Tanaka K. Hanging protocol and viewers for a dental full picture archiving and communication system （PACS）. Dentomaxillofac Radiol. 2007；36（5）：285-295. – reference: 8. Sanjeev BK, Ali Al-ehaideb, Prabhadevi CM, Satish VS, Hosam AB, Sachin CS, Shilpa B. Developments, application, and performance of artificial intelligence in dentistry-A systematic review. Journal of Dental Sciences. 2020；published online. – reference: 9. Muramatsu C, Morishita T, Takahashi R, Hayashi T, Nishiyama W, Ariji Y, Zhou X, Hara T, Katsumata A, Ariji E, Fujita H. Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data. Oral Radiology. 2020；published online. – reference: 2. 有田正博，西田郁子，吉野賢一，中村恵子，小城辰郎，北村知昭，木尾哲朗，大住伴子，坂本英治，庄野庸雄，黒川英雄，安細敏弘，一田利道，佐藤耕一，篠原雄二，瀬田祐司，園木一男，芳賀健輔，村田貴俊，林田 裕，横田 誠，寺下正道，西原達次．九州歯科大学におけるOSCEトライアルの評価．九州歯科学会雑誌．2004：58（6）；213-222. – reference: 16. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific Report. 2019；9（1）：3840. – reference: 5. 日本歯科放射線学会：日本歯科放射線学会規格．口内法撮影X線画像の画像配置，JSOMR X-0001；2015. – reference: 14. Zhang K, Wu KJ, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics. 2018；68：61-70. – reference: 7. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep Convolutional neural networks. Adv Neural Inf Process Syst. 2012；25：1-9. – reference: 11. Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology. 2019；published online. |
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| SubjectTerms | 分類 口内法X線画像 深層学習 解剖学的部位 |
| Title | Deep learningによる口内法X線画像の自動分類 |
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