딥러닝을 이용한 구강 스캐너 이미지 내 치아 영역 실시간 검출
Real-time Tooth Region Detection in Intraoral Scanner Images with Deep Learning
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
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.
Abstract
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.
참고문헌 (19)
- 배은정, "심층 학습을 활용한 가상 치아 이미지 생성 연구 –학습 횟수를 중심으로", 대한치과기공학회지, 42(1), pp.1~8 (2020), 대한치과기공학회[DOI]
- Behera, S. K., "Fruits yield estimation using Faster R-CNN with MIoU", Multimedia Tools and Applications, 80, pp.19043~19056 (2021)
- 최호식, "CAD", CAM의 치과적 응용, 2012, pp.50(3), 110~117[DOI]
- Corbella, S., "Applications of deep learning in dentistry", Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 132(2), pp.225~238 (2021)
- He, K., "Mask r-cnn", Proceedings of the IEEE international confer-ence on computer vision, pp.2961~2969 (2017)
- "On efficient real- time semantic segmentation: A survey"[URL]
- Hong, Y., "Deep dual-reso-lution networks for real-time and accurate semantic seg-mentation of road scenes", arXiv preprint arXiv:2101.06085, ~
- 황미양, "단일 수복물과 3본 고정성 수복물 지대치 모델에서 삼차원 분석을 통한 구강 스캐너의 정확도 비교", 대한치과보철학회지, 57(2), pp.102~109 (2019), 대한치과보철학회[DOI]
- 김재홍, "디지털 인상 채득 시 흔히 발생하는 오류에 관한 연구 - CEREC AC의 사례 중심으로 -", 대한치과기공학회지, 33(3), pp.211~218 (2011), 대한치과기공학회
- Kim, Y. G., "A study on the life expectancy of the dental prosthesis", Journal of Oral Medicine and Pain, 20(2), pp.317~325 (1995)
- Liu, X., "A review of deep-learning-based medical image segmentation methods", Sustainability, 13(3), pp.1224~ (2021)
- 표세욱, "디지털 인상채득 및 CAD", CAM을 이용한 상악 전치부 보철물 제작과 기존 방식 보철물과의 임상적 비교, 2011, pp.49(03), 263~269[DOI]
- Rad, A. E., "Digital dental X-ray image segmentation and feature extraction", Telkomnika Indonesian Journal of Electrical Engineer-ing, 11(6), pp.3109~3114 (2013)
- Silver, D., "Mastering the game of Go with deep neural networks and tree search", Nature, 529(7587), pp.484~489 (2016)
- Xu, J., "PIDNet: A real-time semantic segmentation network inspired by PID controllers", Proceedings of the IEEE, 2023, 19529~19539
- Yang, Y., "Accurate and automatic tooth image segmentation model with deep convolutional neural net-works and level set method", Neurocomputing, 419, pp.108~125 (2021)
- Zhao, H., "Pyramid scene parsing network", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2881~2890 (2017)
- Zhao, M., "Interactive tooth segmentation of dental models", 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp.654~657 (2005)
- Zhu, G., "Tooth detection and segmentation with mask R-CNN", 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp.070~072 (2020)