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M-Count: an application that uses machine learning object detection and color thresholding to count settled mussel
Lance W Miller1, Navaj Nune1, Thomas B LeFevre1
1Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA. curtis.larimer@pnnl.gov.
Analytical Methods : Advancing Methods and Applications
|January 22, 2026
Summary
Quantifying biofouling organisms like mussel larvae is now faster and more accurate with M-Count, a user-friendly application. This tool effectively counts both individual and grouped organisms, improving antifouling performance assessment.
Area of Science:
- Marine biology
- Materials science
- Computational biology
Background:
- Biofouling quantification is challenging, especially for organisms like mussel larvae that form both individuals and clumps.
- Manual counting is time-consuming, and existing automated methods often fail to detect both isolated and grouped organisms or are not user-friendly for non-experts.
- Mussel larvae settlement is a key indicator of antifouling surface performance.
Purpose of the Study:
- To develop a user-friendly, machine learning-based application for accurate biofouling quantification.
- To create a tool capable of detecting and quantifying both individual and grouped fouling organisms in a single workflow.
Main Methods:
- Developed M-Count, an application integrating a neural network for individual organism detection and a color thresholding algorithm for grouped organism detection.
- Applied M-Count to quantify mussel larvae settlement on sample surface images from a biofouling assay.
- Compared M-Count's performance against manual quantification methods.
Main Results:
- M-Count demonstrated significant speed improvement, being 60 times faster than manual counting.
- The application exhibited high consistency, performing tasks repeatedly without bias.
- M-Count maintained good accuracy, with a normalized average maximum residual of 0.220 compared to 0.209 for manual counting.
Conclusions:
- M-Count offers a fast, consistent, and accurate solution for biofouling quantification, particularly for mussel larvae.
- The application's user-friendliness and dual detection capability make it suitable for non-expert users assessing antifouling surfaces.
- This tool enhances the evaluation of antifouling strategies by providing reliable and efficient data on organism settlement.
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