Annotated dataset for deep-learning-based bacterial colony detection

Quantifying bacteria per unit mass or volume is a common task in various fields of microbiology (e.g., infectiology, and food hygiene). Most bacteria can be grown on culture media. The unicellular bacteria reproduce by dividing into two cells, which increases the number of bacteria in the population. Methodologically, this can be followed by culture procedures, which mostly involve determining the number of bacterial colony-forming units on the solid culture media that are visible to the naked eye. However, it is a time-consuming and laborious professional activity. Addressing the automation of colony-forming unit counting by convolutional neural networks in our work, we have cultured 24 bacteria species of veterinary importance with different concentrations on solid media. A total of 56,865 colonies were annotated manually by bounding boxes on the 369 digital images of bacterial cultures. The published database will help developments that use artificial intelligence to automate the counting of bacterial colony-forming units.

定量单位质量或体积内的细菌数量是微生物学各研究领域（如感染病学、食品卫生学）的常规实验任务。多数细菌可在培养基上培养生长。单细胞细菌以二分裂方式增殖，使种群内细菌数量得以扩增。从方法学角度而言，传统培养法大多通过计数固体培养基上肉眼可见的细菌菌落形成单位（colony-forming units，CFU）来实现定量。但该操作耗时耗力，属于专业性较强的工作。本研究针对基于卷积神经网络（convolutional neural networks，CNN）实现菌落形成单位计数自动化的目标，针对24种具有兽医学应用价值的细菌，以不同浓度梯度在固体培养基上进行培养，并针对369张细菌培养物的数字化图像，通过边界框（bounding boxes）手动标注了总计56865个菌落。本公开数据集将助力基于人工智能实现细菌菌落形成单位自动计数相关研究的发展。