Wang Shaofeng, Liang Shuang, Chang Qiao, Zhang Li, Gong Beiwen, Bai Yuxing, Zuo Feifei, Wang Yajie, Xie Xianju, Gu Yu
Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China.
School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
Diagnostics (Basel). 2024 Feb 26;14(5):497. doi: 10.3390/diagnostics14050497.
Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.
准确的牙齿分割和编号是高效自动牙科诊断和治疗的基石。本文提出了一种多任务学习架构，用于全景X射线图像中的准确牙齿分割和编号。应用图卷积网络对目标区域进行自动标注，使用基于改进卷积神经网络的检测子网（DSN）进行牙齿识别和边界回归，并使用有效的区域分割子网（RSSN）进行区域分割。融合使用RSSN和DSN提取的特征以优化边界回归质量，这在多个评估指标上提供了令人印象深刻的结果。具体而言，所提出的框架实现了0.9849的最高F1分数、0.9629的最高Dice指标分数和0.9810的mAP（IOU = 0.5）分数。该框架在提高牙医在牙齿分割和编号任务中的临床效率方面具有很大的前景。
