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Related Experiment Video

Updated: Dec 3, 2025

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Red blood cell classification in lensless single random phase encoding using convolutional neural networks.

Timothy O'Connor, Christopher Hawxhurst, Leslie M Shor

|October 29, 2020

Summary

This summary is machine-generated.

A novel lensless imaging system uses random phase encoding and deep learning for rapid, portable cell identification. This technology significantly improves classification accuracy for red blood cells, outperforming existing methods and offering potential for disease screening.

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Last Updated: Dec 3, 2025

Lensless Fluorescent Microscopy on a Chip

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Published on: August 17, 2011

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

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Published on: February 23, 2018

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Area of Science:

Biophotonics and Imaging
Machine Learning in Biology
Microscopy and Cell Analysis

Background:

Traditional microscopy faces limitations in field of view, numerical aperture, and depth of field due to objective lenses.
Rapid and accurate cell identification is crucial for various applications, including disease screening.
Lensless imaging techniques offer potential advantages over conventional microscopy.

Purpose of the Study:

To develop a compact, field-portable system for rapid cell identification using lensless imaging.
To apply convolutional neural networks (CNNs) for classifying opto-biological signatures of cells.
To evaluate the system's performance against existing classification models and microscopy techniques.

Main Methods:

A 3D-printed, lensless system employing single random phase encoding to capture opto-biological signatures of cells.
Utilizing a diffuser to encode sample information, recorded by a CMOS image sensor.
Inputting captured signatures into a pre-trained AlexNet CNN architecture for classification of animal red blood cells.

Main Results:

The lensless system achieved high classification performance for red blood cells, outperforming Random Forest models and shearing-based 3D digital holographic microscopy.
Convolutional neural networks demonstrated improved classification accuracy, even in the presence of noise.
The system successfully captured 3D biological volumes without lens-imposed restrictions.

Conclusions:

Lensless cell identification using single random phase encoding and CNNs offers a promising approach for rapid and accurate biological analysis.
The developed system is compact, field-portable, and overcomes limitations of traditional microscopy.
This technology represents a significant step towards applications in rapid disease screening.