A generalized deep neural network approach for improving resolution of fluorescence microscopy images

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Zichen Jin, Qing He, Yang Liu, Kaige Wang
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引用次数: 0

Abstract

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264[Formula: see text]nm to 138[Formula: see text]nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.
提高荧光显微镜图像分辨率的广义深度神经网络方法
深度学习能够在成像与重建速度、成像分辨率和成像通量等方面极大地推动超分辨率成像技术的进步。本文提出了一种基于生成对抗网络(GAN)的深度神经网络。生成器采用了基于 U-Net 的网络,并集成了 DenseNet 作为下采样组件。所提出的方法具有优良的特性,例如,该网络模型是用多个不同的生物结构数据集训练出来的;训练出来的模型可以提高不同显微成像模式(如共焦成像和宽视场成像)的成像分辨率;该模型展示了一种泛化能力,即使在数据集之外,也能提高不同生物结构的分辨率。此外,实验结果表明,该方法将洞穴素包覆坑(CCPs)结构的分辨率从264[式:见正文]nm提高到138[式:见正文]nm,提高了1.91倍,并将DNA分子在微流体通道中传输时的成像分辨率提高了近一倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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