Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.

荧光显微镜可以对活细胞和细胞群落进行时空测量。但是，这种潜力尚未完全实现，用于研究致密的三维（3D）细菌生物膜中的单个细胞行为和表型变化。由于荧光显微镜图像中分辨率有限且信噪比低（SBR），因此在密集包装的生物膜中进行准确的细胞检测和细胞形状测量具有挑战性。在这项工作中，我们介绍了细菌细胞形态3D（BCM3D），这是一种将深度学习与数学图像分析相结合的图像分析工作流程，可以准确地对3D荧光图像中的单个细菌细胞进行细分和分类。在BCM3D中，深度卷积神经网络（CNN）使用模拟的生物膜图像和实验上逼真的SBR，细胞密度，标记方法和细胞形状进行训练。我们使用模拟和实验图像系统地评估BCM3D的分割精度。与最新的细菌细胞分割方法相比，BCM3D始终可实现更高的分割精度，并进一步实现了多种群生物膜中的形态细胞自动分类。