论文标题
微分析仪：一种用于自动细菌分析的Python工具，并具有荧光显微镜
MicroAnalyzer: A Python Tool for Automated Bacterial Analysis with Fluorescence Microscopy
论文作者
论文摘要
荧光显微镜是细胞生物学家中广泛使用的方法，用于研究荧光蛋白的定位和共定位。对于微生物细胞生物学家，这些研究通常包括细菌和荧光簇的繁琐和耗时的手动分割或使用多个程序。在这里，我们提出微分析仪 - 一种工具，通过提供微观图像分析的端到端平台来自动化这些任务。尽管确实存在这样的工具，但它们是昂贵的黑盒程序。微分析仪提供了这些工具的开源替代方案，可通过高级用户的灵活性和扩展性。微分析仪基于最新的深度学习分割模型提供精确的细胞和荧光簇分割，并结合了临时处理后处理和菌落 - 一种开源源细胞图像分析工具，用于计算一般细胞和荧光测量。使用这些方法，它的性能比通用方法更好，因为神经网络的动态性质可以快速适应实验限制和假设。其他现有工具不考虑实验假设，也不需要提供荧光簇检测而无需任何专用设备。微型分析仪的关键目标是自动化“从显微镜到数据库”的整个细胞和荧光图像分析的过程，这意味着它不需要研究人员的进一步输入，除了最初的深度学习模型训练。以这种方式，它允许研究人员专注于更大的局面，而不是颗粒状的，眼动劳动
Fluorescence microscopy is a widely used method among cell biologists for studying the localization and co-localization of fluorescent protein. For microbial cell biologists, these studies often include tedious and time-consuming manual segmentation of bacteria and of the fluorescence clusters or working with multiple programs. Here, we present MicroAnalyzer - a tool that automates these tasks by providing an end-to-end platform for microscope image analysis. While such tools do exist, they are costly, black-boxed programs. Microanalyzer offers an open-source alternative to these tools, allowing flexibility and expandability by advanced users. MicroAnalyzer provides accurate cell and fluorescence cluster segmentation based on state-of-the-art deep-learning segmentation models, combined with ad-hoc post-processing and Colicoords - an open-source cell image analysis tool for calculating general cell and fluorescence measurements. Using these methods, it performs better than generic approaches since the dynamic nature of neural networks allows for a quick adaptation to experiment restrictions and assumptions. Other existing tools do not consider experiment assumptions, nor do they provide fluorescence cluster detection without the need for any specialized equipment. The key goal of MicroAnalyzer is to automate the entire process of cell and fluorescence image analysis "from microscope to database", meaning it does not require any further input from the researcher except for the initial deep-learning model training. In this fashion, it allows the researchers to concentrate on the bigger picture instead of granular, eye-straining labor
