Automated Detection and Classification of the Diatom Microscopic Photo Images

Ivanova, Maryna Viktorivna; Vuntesmeri, Yurii Volodymyrovych; Bukhtiiarova, Liudmyla Mykolaivna

doi:10.20535/2523-4455.2019.24.5.187536

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, well-curated taxonomically annotated sets of specimen images resulting from such procedures can provide high quality data sets for training specialists in diatom identification, or also machine learning algorithms, as we demonstrated recently [34]. Computer vision/machine learning techniques have a large potential to further transform and assist the process of diatom analysis, for instance by detection of diatom valves on a virtual slide image, obviating the necessity for the human analyst to draw outline annotations, which in the here presented digital workflow is the most time consuming step [67][68][69][70][71]. Other steps which can expedite the digital variant include clustering diatom valves by their similarities to assist human annotation [17,72]; proposing taxonomic identifications based on taxon classifier models [33]; automatic digital morphometry [67,73,74]; or even by fully automated taxonomic identifications [33,72,[75][76][77][78].…”

Section: The Digital Workflow Provides Comparable Results With the "T...mentioning

confidence: 89%

A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples

Burfeid-Castellanos

Kloster

Beszteri

et al. 2022

Water

View full text Add to dashboard Cite

Diatom identification and counting by light microscopy of permanently embedded acid-cleaned silicate shells (frustules) is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on “digital virtual slides”, and compare it to the traditional, non-digitized light microscopy workflow on freshwater samples. We analysed three replicate slides taken from six benthic samples using two methods: (1) working directly on a light microscope (the “traditional” counting method), and (2) preparing “virtual digital slides” by high-resolution slide scanning and subsequently identifying and labelling individual valves or frustules using a web browser-based image annotation platform (the digital method). Both methods led to comparable results in terms of species richness, diatom indices and diatom community composition. Although counting by digital microscopy was slightly more time consuming, our experience points out that the digital workflow can not only improve the transparency and reusability of diatom counts but it can also increase taxonomic precision. The introduced digital workflow can also be applied for taxonomic inter-expert calibration through the web, and for producing training image sets for deep-learning-based diatom identification, making it a promising and versatile alternative or extension to traditional light microscopic diatom analyses in the future.

show abstract

Section: The Digital Workflow Provides Comparable Results With the "T...mentioning

confidence: 89%

A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples

Burfeid-Castellanos

Kloster

Beszteri

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…Finally, well-curated taxonomically annotated sets of specimen images resulting from such procedures can provide high quality data sets for training specialists in diatom identification, or also machine learning algorithms, as we demonstrated recently [32]. Computer vision / machine learning techniques have a large potential to further transform and assist the process of diatom analysis, for instance by detection of diatom valves on a virtual slide image, obviating the necessity for the human analyst to draw outline annotations, which in the here presented digital workflow is the most time consuming step [68][69][70][71][72]. Other steps which can expedite the digital variant include clustering diatom valves by their similarities to assist human annotation [15,73]; proposing taxonomic identifications based on taxon classifier models [32]; automatic digital morphometry [68,74,75]; or even by fully automated taxonomic identifications [32,73,[76][77][78][79][80].…”

Section: The Digital Workflow Is Slightly More Time Consuming But Ena...mentioning

confidence: 89%

A Digital Light Microscopic Method for Diatom Surveys

Burfeid-Castellanos¹,

Kloster²,

Beszteri³

et al. 2022

Preprint

View full text Add to dashboard Cite

Diatom identification and counting by light microscopy is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on “digital virtual slides”, and compare it to the traditional, non-digitized light microscopy workflow. We analysed three replicates of six samples using two methods: 1) working directly on a light microscope (the “traditional” counting method), and 2) preparing “virtual digital slides” by high-resolution slide scanning and subsequently identifying and labelling individual valves or frustules using a web browser-based image annotation platform (the digital method). Both methods led to comparable results in terms of species richness, diatom indices and diatom community composition. Although counting by digital microscopy was slightly more time consuming, our experience points out that the digital workflow can not only improve the transparency and reusability of diatom counts but it can also increase taxonomic precision. The introduced digital workflow can also be applied for taxonomic inter-expert calibration through the web, and for producing training image sets for deep-learning-based diatom identification, making it a promising and versatile alternative or extension to traditional light microscopic diatom analyses in the future.

show abstract

Automated Detection and Classification of the Diatom Microscopic Photo Images

Cited by 2 publications

References 12 publications

A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples

A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples

A Digital Light Microscopic Method for Diatom Surveys

Contact Info

Product

Resources

About