딥러닝을 이용한 구강 스캐너 이미지 내 치아 영역 실시간 검출

• 영문명: Real-time Tooth Region Detection in Intraoral Scanner Images with Deep Learning
• 발행기관: 한국산업경영시스템학회
• 저자명: 박나윤 김지훈 김태민 송경진 변유진 강민주 전경구 김재곤
• 간행물 정보: 『산업경영시스템학회지』제46권 제3호, 1~6쪽, 전체 6쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.09.30

영문 초록

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

목차

1. 서 론

2. 데이터셋 및 방법론

3. 실험 결과

4. 결 론

References

키워드

Computer Vision Deep Learning Image Segmentation Oral Scanner