Wu Yongjia, Zeng Li, Hong Yaya, Li Xiaojun, Chen Xuepeng
Department of Orthodontics, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Center for Plastic & Reconstructive Surgery, Department of Stomatology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
Digit Health. 2024 Aug 5;10:20552076241269536. doi: 10.1177/20552076241269536. eCollection 2024 Jan-Dec.
Poor conditions in the intraoral environment often lead to low-quality photos and videos, hindering further clinical diagnosis. To restore these digital records, this study proposes a real-time interactive restoration system using segment anything model.
Intraoral digital videos, obtained from the vident-lab dataset through an intraoral camera, serve as the input for interactive restoration system. The initial phase employs an interactive segmentation module leveraging segment anything model. Subsequently, a real-time intraframe restoration module and a video enhancement module were designed. A series of ablation studies were systematically conducted to illustrate the superior design of interactive restoration system. Our quantitative evaluation criteria contain restoration quality, segmentation accuracy, and processing speed. Furthermore, the clinical applicability of the processed videos was evaluated by experts.
Extensive experiments demonstrated its performance on segmentation with a mean intersection-over-union of 0.977. On video restoration, it leads to reliable performances with peak signal-to-noise ratio of 37.09 and structural similarity index measure of 0.961, respectively. More visualization results are shown on the https://yogurtsam.github.io/iveproject page.
Interactive restoration system demonstrates its potential to serve patients and dentists with reliable and controllable intraoral video restoration.
口腔内环境不佳常常导致照片和视频质量低下，阻碍进一步的临床诊断。为了恢复这些数字记录，本研究提出了一种使用分割一切模型的实时交互式恢复系统。
通过口腔内摄像头从vident-lab数据集中获取的口腔内数字视频作为交互式恢复系统的输入。初始阶段采用利用分割一切模型的交互式分割模块。随后，设计了一个实时帧内恢复模块和一个视频增强模块。系统地进行了一系列消融研究，以说明交互式恢复系统的卓越设计。我们的定量评估标准包括恢复质量、分割精度和处理速度。此外，由专家评估处理后视频的临床适用性。
大量实验证明了其在分割方面的性能，平均交并比为0.977。在视频恢复方面，分别实现了可靠的性能，峰值信噪比为37.09，结构相似性指数测量值为0.961。更多可视化结果显示在https://yogurtsam.github.io/iveproject页面上。
交互式恢复系统展示了其为患者和牙医提供可靠且可控的口腔内视频恢复服务的潜力。
