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Updated: Sep 7, 2025

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM

Published on: November 1, 2017

Self-normalized density map (SNDM) for counting microbiological objects.

Krzysztof M Graczyk¹, Jarosław Pawłowski^2,3, Sylwia Majchrowska^2,3

¹Institute for Theoretical Physics, University of Wroclaw, pl. Maxa Borna 9, 50-343, Wrocław, Poland. krzysztof.graczyk@uwr.edu.pl.

Scientific Reports

|June 22, 2022

Summary

This summary is machine-generated.

This study enhances density map (DM) object counting in images using a novel self-normalization module (SNDM). The improved model accurately predicts object counts and shows consistent statistical results, outperforming the original U-Net architecture.

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Area of Science:

Computer Vision
Machine Learning
Microbiology

Background:

Density map (DM) approaches are used for counting microbiological objects in images.
Existing DM models, like U-Net, have limitations in accurately predicting object counts due to statistical uncertainties.

Purpose of the Study:

To analyze the statistical properties and uncertainties of the DM approach for object counting.
To propose an improved DM model that addresses the deficiencies of existing methods.

Main Methods:

Utilized bootstrap and Monte Carlo (MC) dropout for statistical analysis of deep neural networks.
Developed a self-normalization module integrated into the U-Net architecture, creating the Self-Normalized Density Map (SNDM) model.

Main Results:

The Self-Normalized Density Map (SNDM) model demonstrates improved accuracy in predicting the total number of objects.
SNDM exhibits consistent statistical results across bootstrap and MC dropout frameworks, unlike the original model.
The efficiency of SNDM is comparable to established detector-based models like Faster R-CNN and Cascade R-CNN.

Conclusions:

The proposed self-normalization module significantly enhances the accuracy and reliability of density map-based object counting.
SNDM offers a robust and efficient alternative for microbiological object detection and counting in images.