基于双阶段深度学习的全景X线片龋齿自动分割
Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning
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中文总结 AI 辅助
本研究提出结合Faster R-CNN与U-Net的双阶段深度学习框架，用于全景X线片龋齿自动分割，通过系统化转换流程利用3,000张图像训练，实现高精度（IoU 0.9013）并降低假阳性率，解决标注瓶颈，支持临床决策。
中文摘要 AI 辅助
由于传统诊断方法的局限性，早期龋齿检测仍然具有挑战性，尤其是对于后牙邻面病变。深度学习模型在自动龋齿检测方面显示出潜力，但由于需要大量专家标注的训练数据，面临可扩展性限制。本研究提出了一种双阶段深度学习框架，将用于牙齿定位的Faster R-CNN与用于全景X线片中像素级龋齿分割的U-Net相结合。我们开发了一个系统化的转换流程，将大规模多边形标注数据集转换为高分辨率二值分割掩码，从而实现像素级监督学习。该框架使用专家验证的数据集和来自3,000张全景图像的算法处理标签进行训练。我们的方法实现了稳健的性能，IoU为0.9013，Dice系数为0.9482，召回率为0.9433，精确率为0.9774，与现有方法相比表现出更高的准确性，同时显著降低了假阳性率。该双阶段框架有效解决了牙科AI应用中的数据标注瓶颈问题，并展示了可扩展的自动龋齿检测系统的潜力，该系统可以提高诊断一致性并支持临床决策。
英文摘要
Early detection of dental caries remains challenging due to limitations in traditional diagnostic methods, particularly for proximal lesions in posterior teeth. Deep learning models show promise for automated caries detection but face scalability constraints due to requirements for large volumes of expertly annotated training data. This study presents a dual-stage deep learning framework combining Faster R-CNN for tooth localization with U-Net for pixel-wise caries segmentation in panoramic radiographs. We developed a systematic transformation pipeline to convert large-scale polygon-annotated datasets into high-resolution binary segmentation masks, enabling pixel-wise supervised learning. The framework was trained using both expert-verified datasets and algorithmically processed labels from 3,000 panoramic images. Our approach achieved robust performance with an IoU of 0.9013, Dice coefficient of 0.9482, Recall of 0.9433, and Precision of 0.9774, demonstrating superior accuracy compared to existing methods while significantly reducing false-positive rates. The dual-stage framework effectively addresses data annotation bottlenecks in dental AI applications and demonstrates potential for scalable, automated caries detection systems that can improve diagnostic consistency and support clinical decision-making.
发表机构
- Yonsei University（延世大学）
- OUTTA
- The One Star Co., Ltd.（The One Star有限公司）
- WaveString
- Kangwon National University（江原国立大学）
机构由 AI 辅助整理，请以论文原文为准。
