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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

使用深度学习和高内容成像技术对无标签瘤细胞进行分类.

Chawan Piansaddhayanon^1,2,3, Chonnuttida Koracharkornradt², Napat Laosaengpha^1,2

¹Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.

Scientific data

|August 26, 2023

概括

这项研究开发了一种深度学习模型,以在显微镜图像中从正常细胞中识别多样化的癌细胞. 该模型对自动检测循环的瘤细胞具有前景.

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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

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Published on: June 30, 2017

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科学领域:

生物医学工程生物医学工程
计算生物学计算生物学
在瘤学瘤学.

背景情况:

细胞形态分析对于在血液样本中检测循环瘤细胞 (CTC) 是至关重要的.
由于对异质癌细胞系和非血液正常细胞的验证有限,现有的方法往往缺乏稳定性.
需要先进的计算工具来根据形态学准确地区分癌细胞和正常细胞.

研究的目的:

开发和验证一种深度学习模型,用不同的形态特征来区分器官衍生癌细胞和正常细胞.
创建一个全面的微观图像数据集,捕捉癌症和正常细胞的形态异质性.
建立一个用于CTC检测的自动化平台的基础.

主要方法:

构建一个大规模的数据集,包括来自三个胆管癌患者的有机基因癌症和正常细胞的超过75,000张图像.
开发一种概念验证深度学习模型,用于在未标记的显微镜图像中对癌细胞与正常细胞进行分类.
使用接收器操作特性曲线 (AUROC) 下面面积的度量来验证模型的性能.

主要成果:

深度学习模型在将癌细胞与正常细胞区分时获得了0.78的AUROC.
该模型展示了概括能力,在未见患者的细胞图像上表现良好.
开发的数据集为未来CTC检测研究提供了宝贵的资源.

结论:

这项研究提出了一种新的深度学习方法,用于基于形态学的精确癌细胞识别.
该模型的性能表明它有可能提高CTC检测平台的灵敏度和特异性.
这项工作为开发用于癌症诊断的临床应用的自动化,强大的系统奠定了基础.