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Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Updated: Sep 11, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

单细胞的自动图像细分和使用深度学习的索引计算.

Luis A Pena Marquez¹, Subhajit Chakrabarty¹

¹Computer Science Louisiana State University Shreveport Shreveport, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

|August 11, 2025

概括

单细胞指数 (MI) 通过分析红细胞与单细胞的相互作用来帮助输血决策. 一个新的深度学习模型自动计算MI,提高血液分析的效率和准确性.

科学领域:

血液学血液学血液学
医学诊断医学诊断医学诊断
人工智能在医学中的应用

背景情况:

准确的白细胞分类对于医学诊断至关重要,可以识别各种与血液有关的疾病.
单细胞指数 (MI) 对于评估输血兼容性至关重要,因为单细胞可以与红细胞相互作用.
手动血细胞计数是耗时且容易出现错误的,需要自动化解决方案.

研究的目的:

开发一种自动化方法来计算单细胞指数 (MI).
评估使用深度学习来对单细胞和红细胞进行像素级细分的可行性,以计算MI.
为了提高输血决策的血液分析的效率和准确性.

主要方法:

一个定制的显微镜图像数据集被收集和注释.
面具R-CNN深度神经网络模型被训练为自动像素级别的细分.
使用COCO预训练的重量来初始化Mask R-CNN模型.

主要成果:

面具R-CNN模型在自动细分中实现了72%的准确性.
自动化系统证明了能够快速高效地处理大型数据集的能力.
该模型的准确性与人类医学实验室科学家的准确性相当.

关键词:

血液血液血液血液深度学习 (Deep Learning) 是一种深度学习.图像分类图像分类图像分类神经网络的神经网络输血输血输血输血

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Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Published on: October 17, 2018

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Last Updated: Sep 11, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Published on: October 17, 2018

结论:

深度学习,特别是Mask R-CNN,显示了自动化单细胞指数计算的前景.
这种自动化方法可以显著减少实验室的工作量,提高诊断效率.
该系统为临床医生在识别合适的输血候选人方面提供了宝贵的工具.