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241031s2024 xx |||||o 00| ||eng c |
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|a 10.1002/VIW.20230087
|2 doi
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|a pubmed24n1588.xml
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|a (PII)20230087
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Zhu, Enbo
|e verfasserin
|4 aut
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| 245 |
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|a Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multi-organ imaging
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 02.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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 requires 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 multi-organ imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multi-scale 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 multi-organ 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
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|a Journal Article
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|a Artificial intelligence
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|a Biological tissues
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|a Biomedical imaging
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|a Light-sheet fluorescence microscopy
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|a Li, Yan-Ruide
|e verfasserin
|4 aut
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|a Margolis, Samuel
|e verfasserin
|4 aut
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|a Wang, Jing
|e verfasserin
|4 aut
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1 |
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|a Wang, Kaidong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yaran
|e verfasserin
|4 aut
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|a Wang, Shaolei
|e verfasserin
|4 aut
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|a Park, Jongchan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Charlie
|e verfasserin
|4 aut
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| 700 |
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|a Yang, Lili
|e verfasserin
|4 aut
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| 700 |
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|a Chu, Alison
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yuhua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Liang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hsiai, Tzung K
|e verfasserin
|4 aut
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| 773 |
0 |
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