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Updated: Dec 9, 2025

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

Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.

Hong Jin¹, Xinyan Fu², Xinyi Cao³

¹Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, Zhejiang, China.

Journal of Medical Systems

|September 7, 2020

Summary

This summary is machine-generated.

An automated system using machine learning for bone marrow smear analysis significantly speeds up differential cell counts. This digital tool aids in diagnosing hematological diseases with high accuracy.

Keywords:

Bone marrow smear Cell classification Differential cell count Digital image

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Area of Science:

Hematology
Medical Diagnostics
Artificial Intelligence in Medicine

Background:

Manual differential cell counts of bone marrow smears are crucial for diagnosing hematological diseases but are labor-intensive.
Existing methods require significant time and expertise, posing a bottleneck in diagnostic workflows.

Purpose of the Study:

To develop and validate an automated system for differential cell counting on bone marrow smears.
To leverage machine learning and integrated hardware to assist in the diagnosis of hematological disorders.

Main Methods:

An artificial neural network was developed using 3000 retrospective bone marrow smear samples.
The system was validated using 124 new samples, comparing automated recognition with manual pathologist counts.
A large dataset of 600,000 cell images was used for algorithm training and 30,867 for validation.

Main Results:

The automated system achieved an overall cell classification accuracy of 90.1%.
High reliability coefficients (ICC ≥ 0.883) were observed between automated and manual methods for cell series proportion.
Consistent results were found for granulocytes and erythrocytes, demonstrating system effectiveness.

Conclusions:

The developed automated system is effective for cell classification and differential counting on bone marrow smears.
This digital tool offers a valuable aid for screening and evaluating various hematological disorders.
The system has the potential to improve efficiency and accuracy in hematological diagnostics.