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Updated: Jan 24, 2026
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Author Spotlight: Efficient Retinal Ganglion Cell Counting in Mouse Models of Glaucoma for Treatment Evaluation
Published on: October 4, 2024
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Automatic colony cell counting method of phytoplankton in microscopic images with multi-task learning
Renqing Jia1, Gaofang Yin2, Nanjing Zhao3
1Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Optical Monitoring Technology for Environment of Anhui Province, Hefei 230031, China; Wanjiang Emerging Industry Technology Development Center, Tongling 244002, China.
Journal of Microbiological Methods
|January 22, 2026
Summary
This study introduces a new AI method for identifying and counting colonial phytoplankton, improving aquatic ecosystem monitoring. The approach achieves high accuracy in both recognition and cell enumeration, addressing a key challenge in algal analysis.
Area of Science:
- Environmental Science
- Marine Biology
- Computational Biology
Background:
- Phytoplankton are crucial bioindicators for assessing aquatic ecosystem health.
- Automated image-based phytoplankton identification is advancing but struggles with colonial cell counting.
- Accurate species distribution and cell abundance are vital environmental assessment metrics.
Purpose of the Study:
- To develop a novel method for simultaneous phytoplankton recognition and cell counting.
- To overcome the challenges in accurately counting colonial phytoplankton cells.
- To provide a robust tool for automated aquatic algal monitoring.
Main Methods:
- Utilized ResNet50 for deep feature extraction from microscopic images of algal colonies.
- Implemented two parallel branches for classification and counting, with alternating training for parameter updates.
- Evaluated the method on 16 common colonial algae species from Lake Chaohu.
Main Results:
- Achieved 99.2% accuracy in phytoplankton identification.
- Reached 93.9% average accuracy in cell counting.
- Reported mean absolute error of 1.290 and mean squared error of 2.263.
Conclusions:
- The proposed method effectively integrates identification and counting for colonial phytoplankton.
- This unified framework addresses limitations in current automated monitoring tools.
- The approach offers a robust solution for precise aquatic algal assessment.
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