用视频生成技术突破荧光标记数量限制，同时看更多细胞结构
Revealing Microscopic Objects in Fluorescence Live Imaging by Video-to-video Translation Based on A Spatial-temporal Generative Adversarial Network
- 基于时空生成对抗网络，实现显微视频间对象转换
- 成功在有限荧光标记下实现多对象同步可视化
- 适合需要高密度细胞结构成像的生物研究者
尽管荧光显微镜可通过光谱不同的荧光标记同时观察多种亚细胞结构，但标准设备通常只能识别少数微观对象，这主要受限于可用荧光标记的数量。为同时可视化更多对象，本文提出一种基于视频到视频翻译的方法，模拟微观对象的发展过程。本质上，我们采用名为时空生成对抗网络（STGAN）的显微视频翻译框架，揭示微观对象间的时空关系，从而将一个对象的显微视频转换为另一域中不同对象的视频。实验结果表明，所提出的STGAN在显微视频到视频的转换中有效缓解了因荧光标记有限导致的光谱冲突问题，实现了多个微观对象的同时可视化。
原文摘要 · Abstract (English)
In spite of being a valuable tool to simultaneously visualize multiple types of subcellular structures using spectrally distinct fluorescent labels, a standard fluoresce microscope is only able to identify a few microscopic objects; such a limit is largely imposed by the number of fluorescent labels available to the sample. In order to simultaneously visualize more objects, in this paper, we propose to use video-to-video translation that mimics the development process of microscopic objects. In essence, we use a microscopy video-to-video translation framework namely Spatial-temporal Generative Adversarial Network (STGAN) to reveal the spatial and temporal relationships between the microscopic objects, after which a microscopy video of one object can be translated to another object in a different domain. The experimental results confirm that the proposed STGAN is effective in microscopy video-to-video translation that mitigates the spectral conflicts caused by the limited fluorescent labels, allowing multiple microscopic objects be simultaneously visualized.
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