基于口腔自体荧光效应和深度学习的牙菌斑的分割及量化

Segmentation and quantification of dental plaque based on oral autofluorescence effect and deep learning

牙菌斑是牙齿表面一层难以观测的生物膜,是导致龋齿、牙龈炎等一系列疾病的直接诱因。牙菌斑的早期定量化无损检测具有重要的临床意义。短波长光激发下,牙菌斑的细菌及其代谢产物可以产生自体荧光。基于前期成像系统的基础上,采集大量牙菌斑在405nm蓝光激发下产生的红色荧光;牙菌斑越成熟红色荧光强度越高;采用改进的U-net网络对该红色荧光进行分割,对分割出来的牙菌斑进行轮廓提取获得质心,并利用区域生长算法分割出牙菌斑所附着的牙齿,综合牙菌斑成熟程度及面积来评估牙菌斑指数。结果表明改进的U-net网络分割精度优于传统方法。将牙菌斑的面积和成熟程度结合对牙菌斑进行量化,一定程度上可以消除人为诊断的差异性。

Plaque is an unobservable biofilm on the tooth surface,which is a direct cause of a series of diseases such as dental caries and gingivitis.Early quantitative nondestructive detection of dental plaque is of great clinical importance.The bacteria of dental plaque and their metabolites can produce autofluorescence under short wavelength light excitation.The red fluorescence generated by a large amount of plaque under the excitation of 405 nm blue light was collected based on the previous imaging system;the more mature the plaque,the higher the intensity of the red fluorescence;a modified U-net network was used to segment the red fluorescence,and the contour of the segmented plaque was extracted to obtain the core,and the teeth to which the plaque was attached were segmented using the area growth algorithm,and the plaque maturity and area were integrated to evaluate the plaque.The plaque maturity and area were combined to evaluate the plaque index.The results showed that the segmentation accuracy of the improved U-net network was better than that of the traditional method.The combination of plaque area and maturity degree to quantify dental plaque can eliminate the variability of human diagnosis to some extent.