牙体生物膜计算机辅助定量的统计建模方法
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm
摘要：
生物膜是微生物在牙基质上形成的物质。最近在文献中报道了几种量化牙齿生物膜覆盖率的方法，但它们充其量提供了一种半自动化的量化方法，该方法具有来自人类分级者的重要输入，该输入伴随着分级者对前景、背景、生物膜和牙齿的偏见。此外，人类评估指数限制了定量尺度的分辨率;大多数商业尺度使用生物膜覆盖率的五个定量水平（0%、25%、50%、75%和100%）。另一方面，目前最先进的自动斑块量化技术未能进入实际应用，因为它们无法将人类输入纳入处理错误分类。本文提出了一种新的互动方法，生物膜定量光诱导荧光（QLF）图像的犬齿，是独立的感知偏见的分级。该方法将QLF图像划分为称为超像素的均匀纹理和强度的片段;每个超像素被统计建模为单个2D高斯马尔可夫随机场（GMRF）的实现，其参数被估计;然后基于训练数据集将超像素分配到三个类别（背景，生物膜，牙齿基质）中的一个。定量结果具有较高的一致性和精确度。同时，所提出的方法通过单击将错误分类的超像素翻转到不同的状态（背景、牙齿、生物膜）来使病理学家完全控制后处理自动量化，从而提供比简单地标记生物膜和牙齿的边界更大的可用性，如通过当前最先进的方法所做的那样。
英文摘要：
Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semi-automated approach to quantification with significant input from a human grader that comes with the graders bias of what are foreground, background, biofilm, and tooth. Additionally, human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-the-art techniques in automatic plaque quantification fail to make their way into practical applications owing to their inability to incorporate human input to handle misclassifications. This paper proposes a new interactive method for biofilm quantification in Quantitative light-induced fluorescence (QLF) images of canine teeth that is independent of the perceptual bias of the grader. The method partitions a QLF image into segments of uniform texture and intensity called superpixels; every superpixel is statistically modeled as a realization of a single 2D Gaussian Markov random field (GMRF) whose parameters are estimated; the superpixel is then assigned to one of three classes (background, biofilm, tooth substratum) based on the training set of data. The quantification results show a high degree of consistency and precision. At the same time, the proposed method gives pathologists full control to post-process the automatic quantification by flipping misclassified superpixels to a different state (background, tooth, biofilm) with a single click, providing greater usability than simply marking the boundaries of biofilm and tooth as done by current state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
