Background and objective

The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore, highly desirable to reduce the workload of the specialists and facilitate the process.

Methods

This manuscript proposes a novel fully automatic methodology to perform phytoplankton analyses in digital microscopy images of water samples taken with a regular light microscope. In particular, we propose a method capable of analysing multi-specimen images acquired using a simplified systematic protocol. In contrast with prior approaches, this enables its use without the necessity of an expert taxonomist operating the microscope. The system is able to detect and segment the different existing phytoplankton specimens, with high variability in terms of visual appearances, and to merge them into colonies and sparse specimens when necessary. Moreover, the system is capable of differentiating them from other similar objects like zooplankton, detritus or mineral particles, among others, and then classify the specimens into defined target species of interest using a machine learning-based approach.

Results

The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the ”Other” group, a set of relevant toxic and interesting species widely spread over the samples.

Conclusions

The proposed methodology provided accurate results in all the designed steps given the complexity of the problem, particularly in terms of specimen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.

中文翻译：

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数码显微镜图像中浮游植物标本的全自动检测和分类

背景和目标

产生毒素的浮游植物种类的扩散会损害水源的质量。由于常规的水净化技术无效，因此很难检测到这种污染，因此难以中和。目前，浮游植物的水质分析通常由专业人员进行手工常规分析，这是一个主要的限制。浮游植物标本的充分识别和分类需要深入的培训和专业知识。此外，进行的分析涉及冗长的过程，由于无法始终达成专家之间的共识，因此会出现严重的可靠性和可重复性问题。考虑到所有这些因素，因此这些分析的自动化是

方法

该手稿提出了一种新颖的全自动方法，可以在用常规光学显微镜拍摄的水样的数字显微镜图像中进行浮游植物分析。特别是，我们提出了一种能够分析使用简化的系统协议获取的多样本图像的方法。与现有方法相反，这使得它的使用不需要专业的分类学家操作显微镜。该系统能够检测和分割现有的不同浮游植物标本，在视觉外观方面具有很大的可变性，并在必要时将它们合并为菌落和稀疏标本。此外，该系统还可以将它们与其他类似物体区分开来，例如浮游动物，碎屑或矿物颗粒等，

结果

所提出的系统在每个步骤中均提供令人满意且准确的结果。检测步骤提供的FNR为0.4％。浮游植物的检测，即将真实的浮游植物与相似的物体（浮游植物，矿物质等）区分开，在90％的召回率下可提供84.07％的精度。目标物种分类的总体准确性为87.50％。每个物种的召回水平分别是：W。naegeliana，81.82％的螺旋体，5.5％，D。sociale和95％的“ Other”组，这一系列相关的有毒有趣的物种广泛分布在样品。

结论

考虑到问题的复杂性，所提出的方法在所有设计步骤中均提供了准确的结果，尤其是在标本识别，浮游植物分化以及目标物种分类方面。因此，该全自动系统代表了一种强大而一致的工具，可帮助专家分析水质和饮用水的质量。