条件生成对抗性网络对生物组织的虚拟荧光翻译。

IF 3.7 Q2 GENETICS & HEREDITY Phenomics (Cham, Switzerland) Pub Date : 2023-03-02 eCollection Date: 2023-08-01 DOI:10.1007/s43657-023-00094-1
Xin Liu, Boyi Li, Chengcheng Liu, Dean Ta
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引用次数: 0

摘要

荧光标记和成像提供了观察生物组织结构的机会,在组织病理学领域发挥着至关重要的作用。然而,当标记和成像生物组织时,仍然存在一些挑战,例如,耗时的组织制备步骤、昂贵的试剂以及由于光漂白引起的信号偏差。为了克服这些限制,我们提出了一种基于深度学习的组织切片荧光翻译方法,该方法通过条件生成对抗性网络(cGAN)实现。来自小鼠肾组织的实验结果表明,该方法可以从一张原始荧光图像中预测其他类型的荧光图像,并通过合并生成的不同荧光图像来实现虚拟多标记荧光染色。此外,该方法还可以有效地减少成像过程中耗时费力的准备工作,并进一步节省成本和时间。补充信息:在线版本包含补充材料,可访问10.1007/s43657-023-00094-1。
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Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.

Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well. Moreover, this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes, and further saves the cost and time.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00094-1.

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