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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

Lightweight and precise cell classification based on holographic tomography-derived refractive index point cloud.

Haoyuan Wang^1,2,3, Difeng Wu^1,2,3, Miao Zheng^1,2,3

¹Guangdong University of Technology, Institute of Advanced Photonics Technology, School of Information Engineering, Guangzhou, China.

Journal of Biomedical Optics

|September 4, 2025

Summary

This study introduces a novel 3D cell classification method using refractive index (RI) point clouds from holographic tomography. The approach significantly reduces computational load while maintaining high accuracy for applications like disease diagnosis.

Keywords:

cell classification deep learning holographic tomography point cloud refractive index

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Area of Science:

Biomedical Imaging
Computational Biology
Machine Learning

Background:

Accurate cell classification is crucial for disease diagnosis and drug screening.
Three-dimensional (3D) voxel models from holographic tomography offer detailed cellular structures but suffer from high dimensionality, increasing computational demands.
Existing methods face limitations in practical application due to data volume and processing complexity.

Purpose of the Study:

To develop an efficient and accurate cell classification method using 3D refractive index (RI) point cloud data.
To reduce computational complexity without compromising classification performance.
To enable practical application of 3D cell analysis in biomedical research.

Main Methods:

Transformed 3D RI voxel data into point cloud representations using segmented equilibrium sampling.
Developed a deep learning model, RI-PointNet++, specifically for RI point cloud data.
Utilized holographic tomography for data acquisition.

Main Results:

Achieved 93.5% accuracy in classifying HeLa cell viability, outperforming 2D models (87.0%).
Reduced computational complexity by over 99% compared to 3D voxel models (1.49 G floating-point operations).
Demonstrated efficient performance on central processing unit (CPU) hardware.

Conclusions:

The proposed point cloud-based method offers an innovative and lightweight solution for 3D cell classification.
This approach significantly enhances efficiency and accuracy in cell analysis.
Highlights the potential of point cloud methods in biomedical research and diagnostics.