Quantifying biofilm formation on surfaces is challenging because traditional microbiological methods, such as total colony-forming units (CFUs), often rely on manual counting. These are laborious, resource intensive techniques, more susceptible to human error. Confocal laser scanning microscopy (CLSM) is a high-resolution technique that allows 3D visualisation of biofilm architecture. In combination with a live/dead stain, it can be used to quantify biofilm viability on both transparent and opaque surfaces. However, there is little consensus on the appropriate methodology to apply in confocal micrograph processing. In this study, we report the development of an image analysis approach to repeatably quantify biofilm viability and surface coverage. We also demonstrate its use for a range of bacterial species and translational applications. This protocol has been created with ease of use and accessibility in mind, to enable researchers who do not specialise in computational techniques to be confident in applying these methods to analyse biofilm micrographs. Furthermore, the simplicity of the method enables the user to adapt it for their bespoke needs. Validation experiments demonstrate the automated analysis is robust and accurate across a range of bacterial species and an improvement on traditional microbiological analysis. Furthermore, application to translational case studies show the automated method is a reliable measurement of biomass and cell viability. This approach will ensure image analysis is an accessible option for those in the microbiology and biomaterials field, improve current detection approaches and ultimately support the development of novel strategies for preventing biofilm formation by ensuring comparability across studies.

量化表面上的生物膜形成具有挑战性，因为传统的微生物学方法，例如总菌落形成单位 (CFU)，通常依赖于手动计数。这些都是费力的、资源密集型的技术，更容易受到人为错误的影响。共聚焦激光扫描显微镜 (CLSM) 是一种高分辨率技术，可实现生物膜结构的 3D 可视化。结合活/死染色，它可用于量化透明和不透明表面上的生物膜活力。然而，对于应用于共聚焦显微照片处理的适当方法几乎没有达成共识。在这项研究中，我们报告了一种可重复量化生物膜活力和表面覆盖率的图像分析方法的发展。我们还展示了它在一系列细菌物种和转化应用中的用途。该协议的创建考虑到了易用性和可访问性，使不擅长计算技术的研究人员能够自信地应用这些方法来分析生物膜显微照片。此外，该方法的简单性使用户能够根据他们的定制需求进行调整。验证实验表明，自动分析在一系列细菌物种中是可靠和准确的，并且是对传统微生物分析的改进。此外，在转化案例研究中的应用表明，自动化方法是生物量和细胞活力的可靠测量。这种方法将确保图像分析对于微生物学和生物材料领域的人来说是一个可访问的选择，