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

IF 2.3 3区 医学 Q2 OPTICS
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。
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来源期刊
Journal of Innovative Optical Health Sciences
Journal of Innovative Optical Health Sciences OPTICS-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
4.50
自引率
20.00%
发文量
69
审稿时长
>12 weeks
期刊介绍: JIOHS serves as an international forum for the publication of the latest developments in all areas of photonics in biology and medicine. JIOHS will consider for publication original papers in all disciplines of photonics in biology and medicine, including but not limited to: -Photonic therapeutics and diagnostics- Optical clinical technologies and systems- Tissue optics- Laser-tissue interaction and tissue engineering- Biomedical spectroscopy- Advanced microscopy and imaging- Nanobiophotonics and optical molecular imaging- Multimodal and hybrid biomedical imaging- Micro/nanofabrication- Medical microsystems- Optical coherence tomography- Photodynamic therapy. JIOHS provides a vehicle to help professionals, graduates, engineers, academics and researchers working in the field of intelligent photonics in biology and medicine to disseminate information on the state-of-the-art technique.
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