Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Department of Cariology, Restorative Sciences and Endodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA.
Sci Rep. 2018 Aug 29;8(1):13013. doi: 10.1038/s41598-018-31012-5.
Biofilms are surface-attached microbial communities whose architecture can be captured with confocal microscopy. Manual or automatic thresholding of acquired images is often needed to help distinguish biofilm biomass from background noise. However, manual thresholding is subjective and current automatic thresholding methods can lead to loss of meaningful data. Here, we describe an automatic thresholding method designed for confocal fluorescent signal, termed the biovolume elasticity method (BEM). We evaluated BEM using confocal image stacks of oral biofilms grown in pooled human saliva. Image stacks were thresholded manually and automatically with three different methods; Otsu, iterative selection (IS), and BEM. Effects on biovolume, surface area, and number of objects detected indicated that the BEM was the least aggressive at removing signal, and provided the greatest visual and quantitative acuity of single cells. Thus, thresholding with BEM offers a sensitive, automatic, and tunable method to maintain biofilm architectural properties for subsequent analysis.
生物膜是附着在表面的微生物群落，其结构可以用共聚焦显微镜捕获。为了帮助区分生物膜生物量和背景噪声，通常需要对获得的图像进行手动或自动阈值处理。然而，手动阈值处理具有主观性，并且当前的自动阈值处理方法可能会导致有意义的数据丢失。在这里，我们描述了一种针对共聚焦荧光信号设计的自动阈值处理方法，称为生物体积弹性方法（BEM）。我们使用在混合人唾液中生长的口腔生物膜的共聚焦图像堆栈来评估 BEM。手动和自动使用三种不同的方法（Otsu、迭代选择（IS）和 BEM）对图像堆栈进行了阈值处理。生物量、表面积和检测到的物体数量的影响表明，BEM 在去除信号方面的攻击性最小，并且为单个细胞提供了最佳的视觉和定量清晰度。因此，使用 BEM 进行阈值处理为后续分析提供了一种敏感、自动和可调的方法来维持生物膜的结构特性。
