Ying Shunv, Wang Benwu, Zhu Haihua, Liu Wei, Huang Feng
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China.
College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China.
J Dent. 2022 Apr;119:104076. doi: 10.1016/j.jdent.2022.104076. Epub 2022 Feb 23.
Deep learning has been a promising technology in many biomedical applications. In this study, a deep network was proposed aiming for caries segmentation on the clinically collected tooth X-ray images.
The proposed network inherited the skip connection characteristic from the widely used U-shaped network, and creatively adopted vision Transformer, dilated convolution, and feature pyramid fusion methods to enhance the multi-scale and global feature extraction capability. It was then trained on the clinically self-collected and augmented tooth X-ray image dataset, and the dice similarity and pixel classification precision were calculated for the network's performance evaluation.
Experimental results revealed an average dice similarity of 0.7487 and an average pixel classification precision of 0.7443 on the test dataset, which outperformed the compared networks such as UNet, Trans-UNet, and Swin-UNet, demonstrating the remarkable improvement of the proposed network.
This study contributed to the automatic caries segmentation by using a deep network, and highlighted the potential clinical utility value.
深度学习在许多生物医学应用中一直是一项很有前景的技术。在本研究中，提出了一种深度网络，旨在对临床收集的牙齿X线图像进行龋齿分割。
所提出的网络继承了广泛使用的U形网络的跳跃连接特性，并创造性地采用了视觉Transformer、扩张卷积和特征金字塔融合方法，以增强多尺度和全局特征提取能力。然后在临床自行收集并扩充的牙齿X线图像数据集上对其进行训练，并计算骰子相似性和像素分类精度以评估网络性能。
实验结果显示，在测试数据集上，平均骰子相似性为0.7487，平均像素分类精度为0.7443，优于UNet、Trans-UNet和Swin-UNet等对比网络，表明所提出的网络有显著改进。
本研究通过使用深度网络为自动龋齿分割做出了贡献，并突出了其潜在的临床应用价值。
