Centro de investigacion CITIC, Universidade da Coruña, A Coruña 15071, Spain; Grupo VARPA, Instituto de Investigacion Biomédica de A Coruña (INIBIC), Universidade da Coruna, A Coruña 15006, Spain.
Centro de Investigacions Científicas Avanzadas (CICA), Facultade de Ciencias, Universidade da Coruna, 15071 A Coruña, Spain.
Comput Methods Programs Biomed. 2021 Mar;200:105923. doi: 10.1016/j.cmpb.2020.105923. Epub 2021 Jan 15.
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.
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.
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.
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.
产毒浮游植物物种的大量繁殖会影响水源质量。这种污染很难被发现，也很难被中和，因为常规的水净化技术对此无效。目前，浮游植物的水质分析通常由专家通过手动常规分析进行，这是一个主要的局限性。浮游植物标本的准确识别和分类需要经过密集的培训和专业知识。此外，所进行的分析涉及一个冗长的过程，由于专家之间的意见并不总是一致，因此存在严重的可靠性和可重复性问题。考虑到所有这些因素，因此非常希望对这些分析进行自动化，以减少专家的工作量并简化流程。
本文提出了一种新颖的全自动方法，用于对使用常规显微镜拍摄的水样的数字显微镜图像进行浮游植物分析。具体来说，我们提出了一种能够分析使用简化系统方案获取的多标本图像的方法。与以前的方法相比，这使得该方法无需专家在显微镜下操作即可使用。该系统能够检测和分割不同的浮游植物标本，这些标本在外观上存在很大的变化，并且必要时可以将它们合并成群体和稀疏标本。此外，该系统能够将它们与其他类似的物体（如浮游动物、碎屑或矿物质颗粒等）区分开来，然后使用基于机器学习的方法将标本分类为定义的目标感兴趣物种。
所提出的系统在每个步骤都提供了令人满意和准确的结果。检测步骤的假阴性率为 0.4%。浮游植物检测，即区分真正的浮游植物与类似物体（浮游动物、矿物质等），在 90%召回率下提供了 84.07%的精度结果。目标物种分类的总体准确率为 87.50%。每个物种的召回率分别为：W. naegeliana 为 81.82%，A. spiroides 为 57.15%，D. sociale 为 85.71%，“其他”组为 95%，这是一组广泛分布在样本中的相关有毒和有趣的物种。
鉴于问题的复杂性，所提出的方法在所有设计的步骤中都提供了准确的结果，特别是在标本识别、浮游植物区分以及定义的目标物种分类方面。因此，这种全自动系统是一种强大而一致的工具，可以帮助专家分析水源质量和饮用水安全性。
