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Related Experiment Video

Updated: Jul 19, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Improved automated spot counting and modeling with bias correction.

Chun Pang Lin¹, Yajie Duan¹, Davit Sargsyan²

¹Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.

Journal of Biopharmaceutical Statistics

|June 5, 2024

Summary

This study introduces a workflow for accurately counting microbial spots in images, improving accuracy with a novel bias correction method. This method addresses undercounting issues in dense samples, enhancing microbial concentration estimations.

Keywords:

Machine learning bias correction biology colony-forming unit image microorganisms plaque-forming unit simulation spot counting thin-plate spline

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Area of Science:

Microbiology
Image Analysis
Computational Biology

Background:

Accurate enumeration of microorganisms is crucial for biological and medical research.
Automated image analysis offers efficiency but faces challenges with dense or overlapping spots.
Existing methods may suffer from systematic undercounting, particularly in high-concentration samples.

Purpose of the Study:

To develop and validate a complete workflow for estimating microbial concentrations from images.
To introduce and implement an empirical bias correction method to improve automated spot counting accuracy.
To address the undercounting issue caused by spot overlapping in densely populated images.

Main Methods:

Developed a comprehensive image analysis workflow for counting viral plaque forming units (PFU), bacterial colony forming units (CFU), and spot forming units (SFU).
Implemented a novel empirical bias correction method using synthetic images and a thin-plate spline model for training.
Applied the bias correction to the LoST automated spot counting algorithm.

Main Results:

The empirical bias correction method significantly improved the accuracy of automated spot counts.
Bias was substantially reduced for both fixed and random spot sizes and counts.
The workflow demonstrated enhanced performance in estimating microbial concentrations, especially in challenging dense samples.

Conclusions:

The proposed bias correction method effectively mitigates undercounting errors in automated spot counting.
This workflow provides a more accurate and reliable approach for quantifying microorganisms in biological samples.
The findings have implications for various fields requiring precise microbial enumeration from image data.