Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists

Abstract Dentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible...

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Autores principales:	Kazuma Kokomoto, Rena Okawa, Kazuhiko Nakano, Kazunori Nozaki
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a52644fad59b4613a6d976757d759ab5
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spelling	oai:doaj.org-article:a52644fad59b4613a6d976757d759ab52021-12-02T18:02:30ZIntraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists10.1038/s41598-021-98043-32045-2322https://doaj.org/article/a52644fad59b4613a6d976757d759ab52021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98043-3https://doaj.org/toc/2045-2322Abstract Dentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible to utilize intraoral images, progressive growing of generative adversarial networks are used to generate intraoral images. A total of 35,254 intraoral images were used as training data with resolutions of 128 × 128, 256 × 256, 512 × 512, and 1024 × 1024. The results of the training datasets with and without data augmentation were compared. The Sliced Wasserstein Distance was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. 12 pediatric dentists were asked to observe these images and assess whether they were real or generated. The d prime of the 1024 × 1024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512 × 512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that the generated images can be used in dental education or data augmentation for deep learning, without privacy restrictions.Kazuma KokomotoRena OkawaKazuhiko NakanoKazunori NozakiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kazuma Kokomoto Rena Okawa Kazuhiko Nakano Kazunori Nozaki Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
description	Abstract Dentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible to utilize intraoral images, progressive growing of generative adversarial networks are used to generate intraoral images. A total of 35,254 intraoral images were used as training data with resolutions of 128 × 128, 256 × 256, 512 × 512, and 1024 × 1024. The results of the training datasets with and without data augmentation were compared. The Sliced Wasserstein Distance was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. 12 pediatric dentists were asked to observe these images and assess whether they were real or generated. The d prime of the 1024 × 1024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512 × 512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that the generated images can be used in dental education or data augmentation for deep learning, without privacy restrictions.
format	article
author	Kazuma Kokomoto Rena Okawa Kazuhiko Nakano Kazunori Nozaki
author_facet	Kazuma Kokomoto Rena Okawa Kazuhiko Nakano Kazunori Nozaki
author_sort	Kazuma Kokomoto
title	Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
title_short	Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
title_full	Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
title_fullStr	Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
title_full_unstemmed	Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
title_sort	intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a52644fad59b4613a6d976757d759ab5
work_keys_str_mv	AT kazumakokomoto intraoralimagegenerationbyprogressivegrowingofgenerativeadversarialnetworkandevaluationofgeneratedimagequalitybydentists AT renaokawa intraoralimagegenerationbyprogressivegrowingofgenerativeadversarialnetworkandevaluationofgeneratedimagequalitybydentists AT kazuhikonakano intraoralimagegenerationbyprogressivegrowingofgenerativeadversarialnetworkandevaluationofgeneratedimagequalitybydentists AT kazunorinozaki intraoralimagegenerationbyprogressivegrowingofgenerativeadversarialnetworkandevaluationofgeneratedimagequalitybydentists
_version_	1718378886706757632

Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists

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