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Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM

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Published on: November 1, 2017

Focus prediction in digital holographic microscopy using deep convolutional neural networks.

Tomi Pitkäaho, Aki Manninen, Thomas J Naughton

|March 16, 2019

Summary

Deep artificial neural networks can now determine the in-focus depth of cell clusters in digital holograms. This deep learning approach in digital holographic microscopy bypasses complex numerical propagation for faster analysis.

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Area of Science:

Biomedical Imaging
Computational Microscopy
Artificial Intelligence in Science

Background:

Digital holographic microscopy (DHM) is a powerful imaging technique.
Accurate reconstruction depth determination is crucial for analyzing 3D cell structures.
Traditional methods for depth determination can be computationally intensive.

Purpose of the Study:

To explore the application of deep artificial neural networks (ANNs) in DHM.
To address the challenge of determining the in-focus reconstruction depth of cell clusters.
To develop a rapid and accurate method for depth analysis in DHM.

Main Methods:

A deep convolutional neural network (CNN) was trained on a large dataset of hologram amplitude images.
The CNN learned to predict in-focus depths directly from hologram data.
Numerical propagation was not required for depth determination by the trained network.

Main Results:

The trained deep learning model accurately determined the in-focus depth of new holograms with high probability.
The method demonstrated the potential of ANNs to significantly enhance DHM analysis.
This work extends previous findings on deep learning applications in DHM.

Conclusions:

Deep learning offers a promising, efficient alternative for in-focus depth determination in DHM.
ANNs can overcome limitations of traditional numerical reconstruction methods.
This approach facilitates advanced image analysis in digital holographic microscopy.