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通过生成对抗网络加速乳腺组织全样本偏振分辨二次谐波生成成像

Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks.

作者信息

Aghigh Arash, Cardot Jysiane, Mohammadi Melika Saadat, Jargot Gaëtan, Ibrahim Heide, Plante Isabelle, Légaré François

机构信息

Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada.

Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Laval, Québec, Canada.

出版信息

Biomed Opt Express. 2024 Aug 15;15(9):5251-5271. doi: 10.1364/BOE.529779. eCollection 2024 Sep 1.

DOI:10.1364/BOE.529779
PMID:39296390
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11407270/
Abstract

Polarization second harmonic generation (P-SHG) imaging is a powerful technique for studying the structure and properties of biological and material samples. However, conventional whole-sample P-SHG imaging is time consuming and requires expensive equipment. This paper introduces a novel approach that significantly improves imaging resolution under conditions of reduced imaging time and resolution, utilizing enhanced super-resolution generative adversarial networks (ESRGAN) to upscale low-resolution images. We demonstrate that this innovative approach maintains high image quality and analytical accuracy, while reducing the imaging time by more than 95%. We also discuss the benefits of the proposed method for reducing laser-induced photodamage, lowering the cost of optical components, and increasing the accessibility and applicability of P-SHG imaging in various fields. Our work significantly advances whole-sample mammary gland P-SHG imaging and opens new possibilities for scientific discovery and innovation.

摘要

偏振二次谐波产生(P-SHG)成像是研究生物和材料样品结构与特性的强大技术。然而,传统的全样本P-SHG成像耗时且需要昂贵的设备。本文介绍了一种新颖的方法,该方法在减少成像时间和分辨率的条件下显著提高成像分辨率,利用增强型超分辨率生成对抗网络(ESRGAN)对低分辨率图像进行超分辨率处理。我们证明,这种创新方法在保持高图像质量和分析准确性的同时,将成像时间减少了95%以上。我们还讨论了该方法在减少激光诱导的光损伤、降低光学元件成本以及提高P-SHG成像在各个领域的可及性和适用性方面的益处。我们的工作显著推进了全样本乳腺P-SHG成像,并为科学发现和创新开辟了新的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/87913469e612/boe-15-9-5251-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/d450084171fb/boe-15-9-5251-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/a3e80f75c9ae/boe-15-9-5251-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/a5b35d02cc65/boe-15-9-5251-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/77b9b55d8ab1/boe-15-9-5251-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/322e29a7c8c5/boe-15-9-5251-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/ecc5c3576009/boe-15-9-5251-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/87913469e612/boe-15-9-5251-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/d450084171fb/boe-15-9-5251-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/a3e80f75c9ae/boe-15-9-5251-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/a5b35d02cc65/boe-15-9-5251-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/77b9b55d8ab1/boe-15-9-5251-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/322e29a7c8c5/boe-15-9-5251-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/ecc5c3576009/boe-15-9-5251-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d413/11407270/87913469e612/boe-15-9-5251-g007.jpg

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本文引用的文献

1
Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies.用于乳腺微环境研究的非线性显微镜和深度学习分类
Biomed Opt Express. 2023 Apr 21;14(5):2181-2195. doi: 10.1364/BOE.487087. eCollection 2023 May 1.
2
Second harmonic generation microscopy: a powerful tool for bio-imaging.二次谐波产生显微镜:一种用于生物成像的强大工具。
Biophys Rev. 2023 Jan 19;15(1):43-70. doi: 10.1007/s12551-022-01041-6. eCollection 2023 Feb.
3
Improved segmentation of collagen second harmonic generation images with a deep learning convolutional neural network.
深度学习卷积神经网络提高胶原二次谐波图像分割效果。
J Biophotonics. 2022 Dec;15(12):e202200191. doi: 10.1002/jbio.202200191. Epub 2022 Sep 25.
4
The Mammary Gland: Basic Structure and Molecular Signaling during Development.乳腺:发育过程中的基本结构和分子信号
Int J Mol Sci. 2022 Mar 31;23(7):3883. doi: 10.3390/ijms23073883.
5
Collagen polarization promotes epithelial elongation by stimulating locoregional cell proliferation.胶原极化通过刺激局部细胞增殖促进上皮细胞伸长。
Elife. 2021 Oct 18;10:e67915. doi: 10.7554/eLife.67915.
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Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms.基于灰度共生矩阵(GLCM)纹理的利用低空遥感平台进行作物分类
PeerJ Comput Sci. 2021 May 19;7:e536. doi: 10.7717/peerj-cs.536. eCollection 2021.
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Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer.探索胶原蛋白丛林:癌症中纤维组织的生物医学潜力
Bioengineering (Basel). 2021 Jan 21;8(2):17. doi: 10.3390/bioengineering8020017.
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Multidimensional Imaging of Mammary Gland Development: A Window Into Breast Form and Function.乳腺发育的多维成像:洞察乳房形态与功能的窗口
Front Cell Dev Biol. 2020 Mar 31;8:203. doi: 10.3389/fcell.2020.00203. eCollection 2020.
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Deep learning a boon for biophotonics?深度学习对生物光子学是福音吗?
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Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration.Mu-net:用于双光子显微镜图像去噪和恢复的多尺度 U-net。
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