ArticleComputers in biology and medicine2018
Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.
Article in Computers in biology and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Spatial microfluidic holographic integrated platform for label-free and high-dimensional analysis of cancer heterogeneity.Nature communications · 2025Article
- Droplet Microfluidics for Advanced Single-Cell Analysis.Smart medicine · 2025Review
- Rapid alignment-free bacteria identification via optical scattering with LEDs and YOLOv8.Scientific reports · 2024Article
- Label-free white blood cells classification using a deep feature fusion neural network.Heliyon · 2024Article
- Digital in-line holographic microscopy for label-free identification and tracking of biological cells.Military Medical Research · 2024Review
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry.Scientific reports · 2020Article
- Label-Free Identification of White Blood Cells Using Machine Learning.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Label-Free High-Throughput Leukemia Detection by Holographic Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost.
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