Li Shuai, Guo Yuting, Pang Zhennan, Song Wenfeng, Hao Aimin, Xia Bin, Qin Hong
IEEE J Biomed Health Inform. 2022 May;26(5):2240-2251. doi: 10.1109/JBHI.2022.3141773. Epub 2022 May 5.
The accurate detection of dental plaque at an early stage will definitely prevent periodontal diseases and dental caries. However, it remains difficult for the current dental examination to accurately recognize dental plaque without using medical dyeing reagent due to the low contrast between dental plaque and healthy teeth. To combat this problem, this paper proposes a novel network enhanced by a self-attention module for intelligent dental plaque segmentation. The key motivation is to directly utilize oral endoscope images (bypassing the need for dyeing reagent) and get accurate pixel-level dental plaque segmentation results. The algorithm needs to conduct self-attention at the super-pixel level and fuse the super-pixels' local-to-global features. Our newly-designed network architecture will afford the simultaneous fusion of multiple-scale complementary information guided by the powerful deep learning paradigm. The critical fused information includes the statistical distribution of the plaques color, the heat kernel signature (HKS) based local-to-global structure relationship, and the circle-LBP based local texture pattern in the nearby regions centering around the plaque area. To further refine the fuzed multiple-scale features, we devise an attention module based on CNN, which could focalize the regions of interest in plaque more easily, especially for many challenging cases. Extensive experiments and comprehensive evaluations confirm that, for a small-scale training dataset, our method could outperform the state-of-the-art methods. Meanwhile, the user studies verify the claim that our method is more accurate than conventional dental practice conducted by experienced dentists.
早期准确检测牙菌斑肯定能预防牙周疾病和龋齿。然而，由于牙菌斑与健康牙齿之间的对比度较低，目前的牙科检查在不使用医学染色剂的情况下仍难以准确识别牙菌斑。为了解决这个问题，本文提出了一种通过自注意力模块增强的新型网络，用于智能牙菌斑分割。关键动机是直接利用口腔内窥镜图像（无需染色剂）并获得准确的像素级牙菌斑分割结果。该算法需要在超像素级别进行自注意力计算，并融合超像素的局部到全局特征。我们新设计的网络架构将在强大的深度学习范式指导下，实现多尺度互补信息的同时融合。关键的融合信息包括菌斑颜色的统计分布、基于热核特征（HKS）的局部到全局结构关系，以及以菌斑区域为中心的附近区域中基于圆形局部二值模式（circle-LBP）的局部纹理模式。为了进一步细化融合后的多尺度特征，我们设计了一种基于卷积神经网络（CNN）的注意力模块，它可以更轻松地聚焦菌斑中的感兴趣区域，特别是对于许多具有挑战性的情况。大量实验和综合评估证实，对于小规模训练数据集，我们的方法优于现有最先进的方法。同时，用户研究验证了我们的方法比经验丰富的牙医进行传统牙科检查更准确的说法。
