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Related Experiment Video

Updated: Jul 7, 2025

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Published on: December 14, 2009

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Minyoung Baik¹, Sanghoon Shin¹, Samir Kumar¹

¹Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea.

|December 22, 2023

Summary

A new deep learning method using lens-free shadow imaging technology (LSIT) accurately identifies CD34+ cells without staining. This portable, user-friendly approach aids leukemia diagnosis and monitoring for non-experts.

Keywords:

CD34+ classification Cellytics artificial intelligence deep learning label-free cytometry lens-free shadow imaging leukemia diagnosis point-of-care diagnosis

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Last Updated: Jul 7, 2025

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Published on: December 14, 2009

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:

Biomedical Engineering
Computational Biology
Hematology

Background:

Accurate CD34+ cell quantification is crucial for leukemia diagnosis and monitoring.
Current methods like flow cytometry are complex, time-consuming, and require specialized resources.
A need exists for accessible, efficient, and label-free CD34+ cell detection techniques.

Purpose of the Study:

To develop and validate a novel label-free method for CD34+ cell identification using deep learning and lens-free shadow imaging technology (LSIT).
To assess the performance of a custom AlexNet model in classifying CD34+ cells compared to existing deep learning architectures and standard methods.
To evaluate the reliability and correlation of the proposed LSIT-based approach with fluorescence-activated cell sorting (FACS) for CD34+ cell quantification.

Main Methods:

Bone marrow and peripheral blood samples from leukemia patients were processed to isolate mononuclear cells.
A proprietary LSIT device (Cellytics) was used to capture images of cells without staining.
A custom AlexNet deep learning model was trained on the generated dataset to differentiate CD34+ from non-CD34+ cells.

Main Results:

The custom AlexNet model achieved high accuracy (97.3% training, 96.2% validation) in identifying CD34+ cells from 1929 bone marrow images.
The AlexNet model demonstrated superior performance compared to Vgg16 and ResNet50 models.
A strong correlation (R²=0.81) was observed between LSIT-based quantification and FACS, with reliable agreement shown by Bland-Altman analysis.

Conclusions:

Deep learning-powered LSIT offers a groundbreaking, label-free approach for rapid CD34+ cell classification and quantification.
This technology enhances accessibility for non-experts, simplifying leukemia diagnostics and monitoring.
The LSIT method provides a viable, efficient alternative to traditional, complex cell analysis techniques.