Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

基于条件生成对抗网络的癌症干细胞形态学深度学习 [J].

• 影响指数: 6.064
• 发表时间: 06 2020 19
• 来源期刊: Biomolecules
• DOI: 10.3390/biom10060931
• PMID: 32575396
• 文章类型: Journal Article
• 作者列表: Aida S,Okugawa J,Fujisaka S,Kasai T,Kameda H,Sugiyama T

关键词: Cancer stem cell; conditional generative adversarial network; green fluorescence protein; phase contrast; tumor

Mesh: Animals; Deep Learning; Female; Green Fluorescent Proteins / chemistry; Image Processing, Computer-Assisted; Lung Neoplasms / pathology; Mice; Mice, Inbred BALB C; Mice, Nude; Neoplastic Stem Cells / pathology; Optical Imaging; Tumor Cells, Cultured

Abstract

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.

摘要

显微图像分析的深度学习工作流程足以处理上下文变化，因为它们采用生物样本并具有许多任务。使用定义明确的注释图像对于工作流程很重要。通过特异性细胞标志物鉴定癌症干细胞(CSC)。这些CSC广泛表征为肿瘤发展的干细胞(SC)样基因表达和增殖机制。相比之下,形态表征仍然难以捉摸。本研究旨在研究使用条件生成对抗网络（CGAN）的相衬成像中CSC的分割。使用Nanog-Green荧光蛋白的荧光图像训练人工智能（AI），其表达在CSC中得以维持，和相衬图像。AI模型在CSC培养物和肿瘤模型的相衬图像中分割CSC区域。通过选择用于训练的图像，测量分割质量的几个值增加。此外，核荧光叠加相衬对增加值是有效的。我们展示了使用深度学习CGAN工作流将CSC形态映射到非分化条件的可能性。