Depratment of Electrical and Computer engineering, University of California, Riverside, Riverside, CA, United States of America.
Center for Research in Intelligent Systems, University of California, Riverside, Riverside, CA, United States of America.
PLoS One. 2019 Mar 6;14(3):e0212849. doi: 10.1371/journal.pone.0212849. eCollection 2019.
Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson's, Huntington's, diabetes mellitus, etc. hESC are a reliable developmental model for early embryonic growth because of their ability to divide indefinitely (pluripotency), and differentiate, or functionally change, into any adult cell type. Their adaptation to toxicological studies is particularly attractive as pluripotent stem cells can be used to model various stages of prenatal development. Automated detection and classification of human embryonic stem cell in videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. Currently video annotation is done by hand, a process which is very time consuming and exhaustive. To solve this problem, this paper introduces DeephESC 2.0 an automated machine learning approach consisting of two parts: (a) Generative Multi Adversarial Networks (GMAN) for generating synthetic images of hESC, (b) a hierarchical classification system consisting of Convolution Neural Networks (CNN) and Triplet CNNs to classify phase contrast hESC images into six different classes namely: Cell clusters, Debris, Unattached cells, Attached cells, Dynamically Blebbing cells and Apoptically Blebbing cells. The approach is totally non-invasive and does not require any chemical or staining of hESC. DeephESC 2.0 is able to classify hESC images with an accuracy of 93.23% out performing state-of-the-art approaches by at least 20%. Furthermore, DeephESC 2.0 is able to generate large number of synthetic images which can be used for augmenting the dataset. Experimental results show that training DeephESC 2.0 exclusively on a large amount of synthetic images helps to improve the performance of the classifier on original images from 93.23% to 94.46%. This paper also evaluates the quality of the generated synthetic images using the Structural SIMilarity (SSIM) index, Peak Signal to Noise ratio (PSNR) and statistical p-value metrics and compares them with state-of-the-art approaches for generating synthetic images. DeephESC 2.0 saves hundreds of hours of manual labor which would otherwise be spent on manually/semi-manually annotating more and more videos.
人类胚胎干细胞（hESC）来源于囊胚，为许多潜在应用提供了独特的细胞模型。它们在治疗帕金森病、亨廷顿病、糖尿病等疾病方面具有巨大的应用前景。由于 hESC 具有无限分裂（多能性）的能力，并能分化或功能上转变为任何成体细胞类型，因此它们是早期胚胎生长的可靠发育模型。多能干细胞可以用于模拟产前发育的各个阶段，因此它们非常适合用于毒理学研究。在生物学家中，自动检测和分类视频中的人类胚胎干细胞对于定量分析实验工作中 hESC 的各种状态非常感兴趣。目前，视频注释是手动完成的，这是一个非常耗时和费力的过程。为了解决这个问题，本文介绍了 DeephESC 2.0，这是一种自动化机器学习方法，由两部分组成：（a）生成式多对抗网络（GMAN），用于生成 hESC 的合成图像；（b）一个由卷积神经网络（CNN）和三元 CNN 组成的分层分类系统，用于将相差 hESC 图像分类为六个不同的类别，分别是：细胞簇、碎片、未附着细胞、附着细胞、动态出泡细胞和凋亡性出泡细胞。该方法完全是非侵入性的，不需要对 hESC 进行任何化学或染色处理。DeephESC 2.0 能够以 93.23%的准确率对 hESC 图像进行分类，比最先进的方法至少高出 20%。此外，DeephESC 2.0 能够生成大量的合成图像，可用于扩充数据集。实验结果表明，仅在大量的合成图像上训练 DeephESC 2.0 有助于将分类器在原始图像上的性能从 93.23%提高到 94.46%。本文还使用结构相似性（SSIM）指数、峰值信噪比（PSNR）和统计 p 值度量标准来评估生成的合成图像的质量，并将其与最先进的生成合成图像的方法进行比较。DeephESC 2.0 节省了数百个小时的人工劳动，否则这些时间将用于手动/半自动地注释越来越多的视频。
