深度学习赋能的结构光照明超分辨显微技术:原理与应用  

Advancement in Structured Illumination Microscopy Based on Deep Learning

作　　者：黎昕然 陈嘉杰 王美婷 郑晓敏 杜鹏[1] 钟义立 戴小祺 屈军乐[1] 邵永红[1] Li Xinran;Chen Jiajie;Wang Meiting;Zheng Xiaomin;Du Peng;Zhong Yili;Dai Xiaoqi;Qu Junle;Shao Yonghong(College of Physics and Optoelectronic Engineering,Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province,Shenzhen University,Shenzhen 518060,Guangdong,China;College of Biomedical Engineering,Department of Medicine,State-Local Joint Engineering Laboratory of Key Technology of Medical Ultrasound,Key Laboratory of Biomedical Information Detection and Ultrasound Imaging and Guangdong Province,Shenzhen University,Shenzhen 518060,Guangdong,China)

机构地区：[1]深圳大学物理与光电工程学院,光电子器件与系统教育部/广东省重点实验室,广东深圳518060 [2]深圳大学医学部生物医学工程学院,医学超声关键技术国家地方联合工程实验室,广东省生物医学信息检测与超声成像重点实验室,广东深圳518060

出　　处：《中国激光》2024年第21期35-49,共15页Chinese Journal of Lasers

基　　金：国家自然科学基金(62275168,62275164,61775148,62204253,61905145);国家重点研发计划(2022YFA1206300);广东省自然科学基金(2021A1515011916、2023A1515012250);广东省重大人才工程引进类项目(2021QN02Y124);广东省教育厅重点专项(2023ZDZX2052);深圳市科技计划项目(JCYJ20230808104901003,JCYJ20200109105608771);深圳市光子学与生物光子学重点实验室项目(ZDSYS20210623092006020);深圳大学医工交叉研究基金(2023YG002);深圳大学科研仪器研制培育项目(2023YQ008)。

摘　　要：结构光照明显微成像(SIM)技术是一种在超分辨显微成像领域极具代表性的技术。尽管结构光照明能够提升空间分辨率,但其在实现超分辨过程中需要采集多幅图像,而且,诸如样本厚度不一、结构光平移的系统误差及环境噪声等因素均可能会对SIM成像质量产生影响。为解决这些问题,神经网络技术被引入SIM图像处理中。目前,常用的算法模型有卷积神经网络(CNN)及其变体以及生成对抗网络(GAN),它们通过训练样本来分析和纠正上述系统误差,提高时间分辨率,实现快速超分辨成像。据查,目前尚未有文章对于基于深度学习算法的结构光照明显微技术进行综合分析。鉴于此,本文介绍了SIM的基础原理并分析了深度学习算法近年来在提高SIM系统性能方面的应用,并对SIM技术未来的发展方向和面临的挑战进行了前瞻性探讨。Significance Structured illumination microscopy(SIM)is a pivotal technique in super-resolution microscopy as it offers an innovative approach to enhance the spatial resolution exceedingly beyond that achievable by conventional optical microscopes.SIM harnesses the principle of structured illumination,where finely patterned light interacts with the specimen,thereby generating moiréfringes containing high-frequency information that is otherwise unaccessible owing to the diffraction limit.Achieving genuine super-resolution via SIM is involves intricate steps,including capturing numerous low-resolution images under an array of varied illumination patterns.Each of these images encapsulates a unique set of moirépatterns,which serve as the foundation for the subsequent computational reconstruction of a high-resolution image. Although effective, this methodology presentssome challenges. Biological samples, owing to their inherent irregularities and varying tissue thicknesses, can result in considerablevariability in the quality and consistency of the captured moiré patterns. This variability hinders the accurate reconstruction of highresolutionimages. Additionally, systematic errors can further complicate the process, thus potentially introducing artifacts or resultingin the loss of crucial details in the final image.Furthermore, sample damage due to prolonged light exposure must be considered when acquiring multiple images. Hence, thenumber of images required must be minimized without compromising the quality of the super-resolution reconstruction. Determiningthe optimal balance between the number of images and the quality of the final image is key in applying SIM to sensitive biologicalsamples.Image-processing algorithms are widely employed to mitigate the effect of excessive image pairs on imaging results. In addition tothe classical algorithms, recently developed deep-learning algorithms offer promising solutions. Deep-learning algorithms can extractmeaningful information from limited data and efficiently reconstr

关 键 词：结构化照明显微系统 卷积神经网络 超分辨显微成像 图像处理 

分 类 号：O436.1[机械工程—光学工程]