Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive process. Machine learning (ML) techniques are well suited to extract and interpret measurements from multi-dimensional FLIM data sets with substantial improvement in speed over conventional methods. In this topical review, we first discuss the basics of FILM and ML. Second, we provide a summary of lifetime extraction strategies using ML and its applications in classifying and segmenting FILM images with higher accuracy compared to conventional methods. Finally, we discuss two potential directions to improve FLIM with ML with proof of concept demonstrations.

中文翻译：

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机器学习可实现更快，更智能的荧光寿命成像显微镜

荧光寿命成像显微镜（FLIM）是生物医学研究中的一项强大技术，它使用荧光团衰减率在荧光显微镜中提供额外的对比度。但是，目前，FLIM的计算，分析和解释是一个复杂，缓慢且计算昂贵的过程。机器学习（ML）技术非常适合于从多维FLIM数据集中提取和解释测量结果，并且与传统方法相比，其速度有了显着提高。在本主题评估中，我们首先讨论FILM和ML的基础。其次，我们提供了使用ML的生命周期提取策略的摘要，以及与常规方法相比，它在以更高的精度对FILM图像进行分类和分割中的应用。最后，