基于深度学习的荧光显微性能提升(特邀)

Performance enhancement of fluorescence microscopy by using deep learning(invited)

荧光显微镜具有对样品损伤小、可特异性成像等优点,是生物医学研究的主流成像手段。随着人工智能技术的快速发展,深度学习在逆问题求解中取得了巨大成功,被广泛应用于诸多领域。近年来,深度学习在荧光显微成像中的应用掀起了一个研究热潮,为荧光显微技术发展提供了性能上的突破与新思路。基于此,首先介绍了深度学习的基本网络模型,然后对基于深度学习的荧光显微成像技术在荧光显微的空间分辨率、图像采集及重建速度、成像通量和成像质量提升方面的应用进行阐述。最后,对目前深度学习在荧光显微成像中的研究进行总结与展望。

Fluorescence microscopy has the advantage of minimal invasion to bio-samples and visualization of specific structures,and therefore,it has been acting as one of mainstream imaging tools in biomedical research.With the rapid development of artificial intelligence technology,deep learning that has outstanding performance in solving sorts of inverse problems has been widely used in many fields.In recent years,the applications of deep learning in fluorescence microscopy have sprung up,bringing breakthroughs and new insights in the development of fluorescence microscopy.Based on the above,this paper first introduces the basic networks of deep learning,and reviews the applications of deep learning in fluorescence microscopy for improvement of spatial resolution,image acquisition and reconstruction speed,imaging throughput,and imaging quality.Finally,we summarize the research on deep learning in fluorescence microscopy,discuss the remaining challenges,and prospect the future work.