论文标题
机器学习更快，更智能的荧光寿命成像显微镜
Machine learning for faster and smarter fluorescence lifetime imaging microscopy
论文作者
论文摘要
荧光寿命成像显微镜（FLIM）是生物医学研究中的一种强大技术，它使用荧光团衰减速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）技术非常适合从多维FLIM数据集提取和解释测量值，其速度比传统方法的速度有了很大的提高。在这篇局部评论中，我们首先讨论电影和ML的基础知识。其次，与传统方法相比，我们使用ML及其在对薄膜图像进行分类和分割膜图像时提供了终生提取策略的摘要。最后，我们讨论了两个潜在的方向，可以通过ML使用概念证明来改善FLIM。
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.
