The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

活细胞成像显微镜方法的激增为用户提供了许多新的可能性，但也可能具有挑战性。活细胞荧光显微镜面临的主要挑战是捕获细胞内动态，同时保持细胞活力。计算方法可以帮助应对这一挑战，并且正在改变生命系统中可能捕获的边界。在这篇综述中，我们讨论了这些计算方法，重点关注基于人工智能的方法，这些方法可以分层在常用的现有显微镜以及集成计算和显微镜硬件的混合方法之上。我们特别讨论了计算方法如何提高活细胞成像的信噪比、空间分辨率、时间分辨率和多色能力。