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Pipeline for FlowCam data processing with modular open-source software and optional machine learning classification.

Katerina Symiakaki1,2, Tim J W Walles1, Cassidy Park1

  • 1Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany.

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|March 30, 2026
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Summary

A new, free, multi-platform pipeline simplifies processing of FlowCam plankton imaging data. This tool enhances reproducibility and accessibility for high-throughput plankton analysis, enabling wider scientific use of imaging data.

Keywords:
Artificial intelligenceFlowCamImagingImaging flowcytometryMachine learning classificationOpen source softwarePlankton

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Area of Science:

  • Marine Biology
  • Ecology
  • Oceanography
  • Aquatic Sciences

Background:

  • Imaging instruments like FlowCam offer advantages for plankton research, including high-throughput sample processing and quantitative trait data collection.
  • Traditional microscopy methods for plankton analysis are time-consuming and prone to human bias.
  • Existing software for FlowCam data processing, such as VisualSpreadsheet (VSP), has limitations including cost, platform exclusivity, and restricted machine learning support.

Purpose of the Study:

  • To develop a freely available, multi-platform, modular pipeline for processing data from FlowCam imaging instruments.
  • To overcome the limitations of commercial software (VSP) by offering enhanced functionalities and broader system compatibility.
  • To facilitate reproducible, high-throughput plankton analysis and increase the accessibility of FlowCam data for scientific research.

Main Methods:

  • A preprocessing Python script was developed to unify FlowCam data from different VSP versions, detect duplicate images, and calculate biovolume using a distance map algorithm.
  • An open-source, cross-platform program called LabelChecker was created for displaying, annotating, and validating FlowCam images and their associated data.
  • The pipeline integrates with machine learning approaches for automated image classification, with results stored in a unified CSV file compatible with LabelChecker.

Main Results:

  • The developed pipeline successfully processes FlowCam data from various instruments and VSP versions, unifying output and enabling efficient data management.
  • LabelChecker provides a user-friendly interface for image annotation and validation, improving the quality control of plankton datasets.
  • The integrated pipeline, including preprocessing, LabelChecker, and machine learning, demonstrated effective workflow for plankton image classification and analysis on sample datasets.

Conclusions:

  • This freely available, modular pipeline significantly enhances the accessibility and reproducibility of high-throughput plankton analysis using FlowCam data.
  • The developed tools facilitate wider adoption and utilization of FlowCam instruments and their associated data, boosting scientific output in aquatic research.
  • The pipeline's adaptability to different FlowCam versions and potential for integration with other imaging systems makes it a valuable resource for diverse plankton studies.