摘要
Background:Fluorescence microscopy has increasingly promising applications in life science.This bibliometrics-based review focuses on deep learning assisted fluorescence microscopy imaging techniques.Methods:Papers on this topic retrieved by Core Collection on Web of Science between 2017 and July 2022 were used for the analysis.In addition to presenting the representative papers that have received the most attention,the process of development of the topic,the structure of authors and institutions,the selection of journals,and the keywords are analyzed in detail in this review.Results:The analysis found that this topic gained immediate popularity among scholars from its emergence in 2017,gaining explosive growth within three years.This phenomenon is because deep learning techniques that have been well established in other fields can be migrated to the analysis of fluorescence micrographs.From 2020 onwards,this topic tapers off but has attracted a few stable research groups to tackle the remaining challenges.Although this topic has been very popular,it has not attracted scientists from all over the world.The USA,China,Germany,and the UK are the key players in this topic.Keyword analysis and clustering are applied to understand the different focuses on this topic.Conclusion:Based on the bibliometric analysis,the current state of this topic to date and future perspectives are summarized at the end.
