Microscopy research and technique. 2024 Nov 9. doi: 10.1002/jemt.24732 Q12.02024

Slicing Network for Wide-Field Fluorescence Image Based on the Improved U-Net Model

基于改进的U-Net模型的宽场荧光图像切片网络翻译改进

Shiqing Yao¹, Meiling Guan^{2 3}, Wei Ren², Peng Xi², Meiqi Li^{2 4}, Mingjian Sun¹

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作者单位

¹ Control Science and Engineering, Harbin Institute of Technology, Weihai, China.

² Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.

³ Key Laboratory of Computational Optical Imaging Technology, Chinese Academy of Sciences, Beijing, China.

⁴ School of Life Sciences, Peking University, Beijing, China.

DOI: 10.1002/jemt.24732 PMID: 39520144

摘要中英对照阅读

Fluorescence imaging stands as a pivotal component in biomedical research, requiring the elimination of out-of-focus background noise resulting from wide-field volumetric illumination of the whole field-of-view and scattering within thick biological tissues. Traditional methods struggle to effectively address varying degrees of defocusing in fluorescence images. This study introduces the utilization of upU-Net, 3D U-Net, and 3D upU-Net as defocusing networks tailored for 2D and 3D wide-field fluorescence images, yielding notable enhancements. These advancements facilitate more economically viable confocal microscopy, delivering significant advantages to biologists presently utilizing wide-field fluorescence microscopy.

Keywords: deep learning defocusing; fluorescence image processing; fluorescence microscopy.

Keywords：wide-field fluorescence image; slicing network; U-Net model

荧光成像在生物医学研究中扮演着关键角色，需要消除由宽场体积照明整个视野以及厚生物组织内部散射引起的焦外背景噪声。传统方法难以有效解决荧光图像中存在的不同程度的离焦问题。本研究引入了upU-Net、3D U-Net和3D upU-Net作为针对2D和3D宽场荧光图像的离焦网络，取得了显著改进。这些进展有助于提供更具成本效益的共聚焦显微镜解决方案，为目前使用宽场荧光显微镜的生物学家带来了重大优势。

关键词：深度学习去离焦；荧光图像处理；荧光显微镜。

关键词：宽场荧光图像; 切片网络; U-Net模型

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期刊名：Microscopy research and technique

缩写：MICROSC RES TECHNIQ

ISSN：1059-910X

e-ISSN：1097-0029

IF/分区：2.0/Q1

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