{"title":"Recent Advances in Structured Illumination Microscopy: From Fundamental Principles to AI-Enhanced Imaging.","authors":"Heng Zhang, Yunqi Zhu, Luhong Jin, Haixu Yang, Jianhang Wang, Sergey Ablameyko, Xu Liu, Yingke Xu","doi":"10.1002/smtd.202401616","DOIUrl":null,"url":null,"abstract":"<p><p>Structured illumination microscopy (SIM) has emerged as a pivotal super-resolution technique in biological imaging. This review aims to introduce the fundamental principles of SIM, primarily focuses on the latest developments in super-resolution SIM imaging, such as the light illumination and modulation devices, and the image reconstruction algorithms. Additionally, the application of deep learning (DL) technology in SIM imaging is explored, which is employed to enhance image quality, accelerate imaging and reconstruction speed or replace the current image reconstruction method. Furthermore, the key evaluation metrics are proposed and discussed for assessment of deep-learning neural networks, especially for their employment in SIM. Finally, the future integration of artificial intelligence (AI) with SIM system and the perspective of smart microscope are also discussed.</p>","PeriodicalId":229,"journal":{"name":"Small Methods","volume":" ","pages":"e2401616"},"PeriodicalIF":10.7000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small Methods","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/smtd.202401616","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Recent Advances in Structured Illumination Microscopy: From Fundamental Principles to AI-Enhanced Imaging.
Structured illumination microscopy (SIM) has emerged as a pivotal super-resolution technique in biological imaging. This review aims to introduce the fundamental principles of SIM, primarily focuses on the latest developments in super-resolution SIM imaging, such as the light illumination and modulation devices, and the image reconstruction algorithms. Additionally, the application of deep learning (DL) technology in SIM imaging is explored, which is employed to enhance image quality, accelerate imaging and reconstruction speed or replace the current image reconstruction method. Furthermore, the key evaluation metrics are proposed and discussed for assessment of deep-learning neural networks, especially for their employment in SIM. Finally, the future integration of artificial intelligence (AI) with SIM system and the perspective of smart microscope are also discussed.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.