Counting microalgae cultures with a stereo microscope and a cell phone using deep learning online resources

Bulletin of the National Research Centre

Research

Miguel Barbosa¹  Ana Amorim²  Maria da Conceição Proença³ 

[1] MARE-Marine and Environmental Sciences Centre/ARNET-Aquatic Research Network, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal;MARE-Marine and Environmental Sciences Centre/ARNET-Aquatic Research Network, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal;Departamento de Biologia Vegetal, Faculdade de Ciências, Universidade de Lisboa, C2-FCUL, 1749-016, Lisbon, Portugal;MARE-Marine and Environmental Sciences Centre/ARNET-Aquatic Research Network, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal;Departamento de Física, C8-FCUL, 1749-016, Lisbon, Portugal;

关键词: Microalgae cultures;    Object detection;    Image processing;    Deep learning;    Semi-automated cell count;   

DOI  :  10.1186/s42269-022-00965-z

 received in 2022-10-17, accepted in 2022-12-01,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThis work presents an experience done to evaluate the number of very small objects in the field of view of a stereo microscope, which are usually counted by direct observation, with or without the use of grids as visual aids. We intend to show that deep learning recent algorithms like YOLO v5 are adequate to use in the evaluation of the number of objects presented, which can easily reach the 1000 s. This kind of algorithm is open-source software, requiring a minimum of skills to install and run on a regular laptop. We further intend to show that the robustness of these kinds of approaches using convolutional neural networks allowed for the use of images of less quality, such as the images acquired with a cell phone.ResultsThe results of training the algorithm and counting microalgae in cell phone images were assessed through human curation in a set of test images and showed a high correlation, showing good precision and accuracy in detections.ConclusionsThis is a low-cost alternative available worldwide to many more facilities than expensive cameras and high-maintenance rigid set-ups, along with software packages with a slow learning curve, therefore enlarging the scope of this technique to areas of knowledge where the conditions of laboratory and human work are a limiting factor.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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