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Related Experiment Video

Updated: Jul 19, 2025

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

Deep learning-based label-free hematology analysis framework using optical diffraction tomography.

Dongmin Ryu¹, Taeyoung Bak², Daewoong Ahn¹

¹Tomocube Inc., Daejeon, 34109, Republic of Korea.

|August 14, 2023

Summary

This summary is machine-generated.

This study introduces a novel label-free hematology analysis framework using optical diffraction tomography and deep learning. The method accurately detects and classifies blood cells, offering a faster, cost-effective alternative to traditional staining methods.

Keywords:

Deep learning Hematology analysis Label-free imaging Object detection Optical diffraction tomography

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Last Updated: Jul 19, 2025

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Area of Science:

Biomedical Engineering
Computational Biology
Medical Diagnostics

Background:

Traditional hematology analysis relies on time-consuming and labor-intensive chemical staining processes.
Label-free imaging presents a cost-effective and efficient alternative for hematology analysis.

Purpose of the Study:

To develop a label-free hematology analysis framework using optical diffraction tomography and deep learning.
To accurately detect and classify various blood cell types without chemical staining.

Main Methods:

Utilized optical diffraction tomography for label-free imaging of blood cells.
Employed the fully convolutional one-stage object detector (FCOS), a deep learning model, for cell detection and classification.
Classified detected cells into four groups: red blood cells, abnormal red blood cells, platelets, and white blood cells.

Main Results:

The object detection model achieved a mean average precision (mAP) of 0.977 for blood cell detection.
Achieved high accuracy in four-class blood cell classification with a weighted F1 score of 0.9708 and total accuracy of 0.9712.
Demonstrated reasonable correlation with reference equipment for Mean Corpuscular Volume (MCV) (0.905) and Mean Corpuscular Hemoglobin (MCH) (0.889).

Conclusions:

The proposed framework successfully demonstrates label-free detection and classification of blood cells using optical diffraction tomography.
This approach offers a promising, efficient, and cost-effective alternative to conventional hematology analysis.
The study validates the potential of integrating advanced imaging and deep learning for improved diagnostic tools.