Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation
Abstract Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78190-z |
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| author | Soo-Yeon Jeong Eun-Jeong Bae Hyun Soo Jang SeongJu Na Sun-Young Ihm |
| author_facet | Soo-Yeon Jeong Eun-Jeong Bae Hyun Soo Jang SeongJu Na Sun-Young Ihm |
| author_sort | Soo-Yeon Jeong |
| collection | DOAJ |
| description | Abstract Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data. |
| format | Article |
| id | doaj-art-e924f5feb0de4867afe6a570d0d2d7fc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e924f5feb0de4867afe6a570d0d2d7fc2024-11-10T12:26:47ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-78190-zStudy on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentationSoo-Yeon Jeong0Eun-Jeong Bae1Hyun Soo Jang2SeongJu Na3Sun-Young Ihm4Division of Software Engineering, Pai Chai UniversityDepartment of Dental Technology, Bucheon UniversityITCEN COREDepartment of Computer Engineering, Pai Chai UniversityDepartment of Computer Engineering, Pai Chai UniversityAbstract Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data.https://doi.org/10.1038/s41598-024-78190-zData augmentationGAN (generative adversarial network)Image inpaintingCR-FillPix2pix |
| spellingShingle | Soo-Yeon Jeong Eun-Jeong Bae Hyun Soo Jang SeongJu Na Sun-Young Ihm Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation Scientific Reports Data augmentation GAN (generative adversarial network) Image inpainting CR-Fill Pix2pix |
| title | Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation |
| title_full | Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation |
| title_fullStr | Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation |
| title_full_unstemmed | Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation |
| title_short | Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation |
| title_sort | study on virtual tooth image generation utilizing cf fill and pix2pix for data augmentation |
| topic | Data augmentation GAN (generative adversarial network) Image inpainting CR-Fill Pix2pix |
| url | https://doi.org/10.1038/s41598-024-78190-z |
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