From Pixels to Information: Artificial Intelligence in Fluorescence Microscopy

IF 3.7 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Advanced Photonics Research Pub Date : 2024-07-15 DOI:10.1002/adpr.202300308
Seungjae Han, Joshua Yedam You, Minho Eom, Sungjin Ahn, Eun-Seo Cho, Young-Gyu Yoon
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Abstract

This review explores how artificial intelligence (AI) is transforming fluorescence microscopy, providing an overview of its fundamental principles and recent advancements. The roles of AI in improving image quality and introducing new imaging modalities are discussed, offering a comprehensive perspective on these changes. Additionally, a unified framework is introduced for comprehending AI-driven microscopy methodologies and categorizing them into linear inverse problem-solving, denoising, and nonlinear prediction. Furthermore, the potential of self-supervised learning techniques that address the challenges associated with training the networks are explored, utilizing unlabeled microscopy data to enhance data quality and expand imaging capabilities. It is worth noting that while the specific examples and advancements discussed in this review focus on fluorescence microscopy, the general approaches and theories are directly applicable to other optical microscopy methods.

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从像素到信息:荧光显微镜中的人工智能
这篇综述探讨了人工智能(AI)如何改变荧光显微技术,概述了其基本原理和最新进展。文章讨论了人工智能在提高图像质量和引入新成像模式方面的作用,为这些变化提供了一个全面的视角。此外,还介绍了一个统一的框架,用于理解人工智能驱动的显微镜方法,并将其分为线性逆向问题解决、去噪和非线性预测。此外,还探讨了自监督学习技术的潜力,这些技术可以利用未标记的显微镜数据来提高数据质量并扩展成像能力,从而解决与训练网络相关的挑战。值得注意的是,虽然本综述中讨论的具体例子和进展侧重于荧光显微镜,但一般方法和理论可直接适用于其他光学显微镜方法。
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