치아 보철물 디자인을 위한 이미지 대 이미지 변환 GAN 모델

• 영문명: An Image-to-Image Translation GAN Model for Dental Prothesis Design
• 발행기관: 한국IT서비스학회
• 저자명: 김태민 김재곤
• 간행물 정보: 『한국IT서비스학회지』제22권 제5호, 87~98쪽, 전체 12쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2023.10.31

영문 초록

Traditionally, tooth restoration has been carried out by replicating teeth using plaster-based materials. However, recent technological advances have simplified the production process through the introduction of computer-aided design(CAD) systems. Nevertheless, dental restoration varies among individuals, and the skill level of dental technicians significantly influences the accuracy of the manufacturing process. To address this challenge, this paper proposes an approach to designing personalized tooth restorations using Generative Adversarial Network(GAN), a widely adopted technique in computer vision. The primary objective of this model is to create customized dental prosthesis for each patient by utilizing 3D data of the specific teeth to be treated and their corresponding opposite tooth. To achieve this, the 3D dental data is converted into a depth map format and used as input data for the GAN model. The proposed model leverages the network architecture of Pixel2Style2Pixel, which has demonstrated superior performance compared to existing models for image conversion and dental prosthesis generation. Furthermore, this approach holds promising potential for future advancements in dental and implant production.

목차

1. 서 론

2. 이론적 배경

3. 데이터 생성 방법 및 모델 설명

4. 실험 및 결과

5. 결 론

참고문헌

키워드

Dental prosthesis design Generative AI Image GAN