Enbo Zhu, Yan-Ruide Li, Samuel Margolis, Jing Wang, Kaidong Wang, Yaran Zhang, Shaolei Wang, Jongchan Park, Charlie Zheng, Lili Yang, Alison Chu, Yuhua Zhang, Liang Gao, Tzung K. Hsiai
{"title":"用于揭示生物现象和多器官成像的人工智能定向光片显微镜的前沿领域","authors":"Enbo Zhu, Yan-Ruide Li, Samuel Margolis, Jing Wang, Kaidong Wang, Yaran Zhang, Shaolei Wang, Jongchan Park, Charlie Zheng, Lili Yang, Alison Chu, Yuhua Zhang, Liang Gao, Tzung K. Hsiai","doi":"10.1002/viw.20230087","DOIUrl":null,"url":null,"abstract":"Light-sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3-D imaging of large-scale biological tissues and 4-D (space + time) imaging of small live animals. The large data associated with LSFM require efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML-directed LSFM is an emerging area for multiorgan imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multiscale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep-tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multiorgan imaging to tumor diagnosis. In addition, this review will address AI/ML-directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post-imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research.","PeriodicalId":34127,"journal":{"name":"VIEW","volume":null,"pages":null},"PeriodicalIF":9.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multiorgan imaging\",\"authors\":\"Enbo Zhu, Yan-Ruide Li, Samuel Margolis, Jing Wang, Kaidong Wang, Yaran Zhang, Shaolei Wang, Jongchan Park, Charlie Zheng, Lili Yang, Alison Chu, Yuhua Zhang, Liang Gao, Tzung K. Hsiai\",\"doi\":\"10.1002/viw.20230087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Light-sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3-D imaging of large-scale biological tissues and 4-D (space + time) imaging of small live animals. The large data associated with LSFM require efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML-directed LSFM is an emerging area for multiorgan imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multiscale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep-tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multiorgan imaging to tumor diagnosis. In addition, this review will address AI/ML-directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post-imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research.\",\"PeriodicalId\":34127,\"journal\":{\"name\":\"VIEW\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VIEW\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/viw.20230087\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VIEW","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/viw.20230087","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multiorgan imaging
Light-sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3-D imaging of large-scale biological tissues and 4-D (space + time) imaging of small live animals. The large data associated with LSFM require efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML-directed LSFM is an emerging area for multiorgan imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multiscale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep-tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multiorgan imaging to tumor diagnosis. In addition, this review will address AI/ML-directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post-imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research.
期刊介绍:
View publishes scientific articles studying novel crucial contributions in the areas of Biomaterials and General Chemistry. View features original academic papers which go through peer review by experts in the given subject area.View encourages submissions from the research community where the priority will be on the originality and the practical impact of the reported research.