Ma Tian, Dang Zhenrui, Yang Yizhou, Yang Jiayi, Li Jiahui
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an,Shaanxi, , China.
Digit Health. 2024 Sep 10;10:20552076241277154. doi: 10.1177/20552076241277154. eCollection 2024 Jan-Dec.
To achieve an accurate assessment of orthodontic and restorative treatments, tooth segmentation of dental panoramic X-ray images is a critical preliminary step, however, dental panoramic X-ray images suffer from poorly defined interdental boundaries and low root-to-alveolar bone contrast, which pose significant challenges to tooth segmentation. In this article, we propose a multi-feature coordinate position learning-based tooth image segmentation method for tooth segmentation.
For better analysis, the input image is randomly flipped horizontally and vertically to enhance the data. Our method extracts multi-scale tooth features from the designed residual omni-dimensional dynamic convolution and the designed two-stream coordinate attention module can further complement the tooth boundary features, and finally the two features are fused to enhance the local details of the features and global contextual information, which achieves the enrichment and optimization of the feature information.
The publicly available adult dental datasets Archive and Dataset and Code were used in the study. The experimental results were 87.96% and 92.04% for IoU, 97.79% and 97.32% for ACC, and 86.42% and 95.64% for Dice.
The experimental results show that the proposed network can be used to assist doctors in quickly viewing tooth positions, and we also validate the effectiveness of the proposed two modules in fusing features.
为了实现对正畸和修复治疗的准确评估，牙科全景X线图像的牙齿分割是关键的初步步骤，然而，牙科全景X线图像存在牙间隙边界定义不清和牙根与牙槽骨对比度低的问题，这给牙齿分割带来了重大挑战。在本文中，我们提出了一种基于多特征坐标位置学习的牙齿图像分割方法用于牙齿分割。
为了更好地进行分析，对输入图像进行水平和垂直随机翻转以增强数据。我们的方法从设计的残差全维动态卷积中提取多尺度牙齿特征，并且设计的双流坐标注意力模块可以进一步补充牙齿边界特征，最后将这两个特征融合以增强特征的局部细节和全局上下文信息，从而实现特征信息的丰富和优化。
研究中使用了公开可用的成人牙齿数据集Archive和Dataset and Code。实验结果IoU分别为87.96%和92.04%，ACC分别为97.79%和97.32%，Dice分别为86.42%和95.64%。
实验结果表明，所提出的网络可用于协助医生快速查看牙齿位置，并且我们还验证了所提出的两个模块在融合特征方面的有效性。
