基于轻量级神经网络的结构照明显微镜微光成像重建。

IF 1.9 4区 工程技术 Q3 MICROSCOPY
Hesong Jiang, Peihong Wu, Juan Zhang, Xueyuan Wang, Jinkun Zhan, Hexuan Tang
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引用次数: 0

摘要

结构照明显微镜作为一种超分辨率光学显微镜技术,已广泛应用于生物物理学、神经科学和细胞生物学等领域的研究。然而,这种技术通常需要高强度照明和多个图像采集来生成单个高分辨率图像。这一过程不仅显著降低了成像速度,而且增加了样品在强光下的曝光时间,导致光毒性和光漂白问题增加,在活细胞成像中尤其突出。在此,我们提出了一种轻量级的多卷积UNet (MCU-Net),旨在通过减少模型参数数量来保持高效的超分辨率重建性能。该算法将多种卷积技术与多尺度注意机制相结合,增强了模型对不同尺度信息的敏感性,提高了模型对图像纹理和结构的精确识别能力,在弱光条件下也能实现高质量的超分辨率重建。在不同细胞类型、光照强度和各种测试集上,将MCU-Net与深度神经网络模型(UNet、ScUNet、EDSR、DFCAN)和传统重建算法(Wiener、HiFi、TV)进行比较,从效率和准确性方面对模型的整体性能进行了评估。实验结果表明,与其他深度学习模型相比,MCU-Net在MS-SSIM上提高了12.66%,在NRMSE指数上提高了50.79%。即使在低信噪比输入的情况下,其预测精度也保持稳定。此外,它在重建速度和模型精度之间取得了最佳平衡,与DFCAN模型相比,推理速度提高了76.10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstruction of structured illumination microscopy for live imaging in low light with lightweight neural networks.

Structured illumination microscopy (SIM) as a type of super-resolution optical microscopy technique has been widely used in the fields of biophysics, neuroscience, and cell biology research. However, this technique often requires high-intensity illumination and multiple image acquisitions to generate a single high-resolution image. This process not only significantly reduces the imaging speed, but also increases the exposure time of samples to intense light, leading to increased phototoxicity and photobleaching issues, especially prominent in live cell imaging. Here, we propose a lightweight Multi-Convolutional UNet (MCU-Net) aiming to maintain efficient super-resolution reconstruction performance by reducing the model parameter quantity. The algorithm integrates multiple convolutional techniques with multi-scale attention mechanisms, enhancing the model's sensitivity to information at different scales and improving its precise recognition ability for image textures and structures, thus enabling high-quality super-resolution reconstruction even under low-light conditions. The overall performance of the model is evaluated in terms of efficiency and accuracy, comparing MCU-Net with deep neural network models (UNet, ScUNet, EDSR, DFCAN) and traditional reconstruction algorithms (Wiener, HiFi, TV) across different cell types, lighting intensities, and various test sets. Experimental results show that compared to other deep learning models, MCU-Net achieves a 12.66% improvement in MS-SSIM and a 50.79% increase in NRMSE index. Its prediction accuracy remains stable even in the presence of low signal-to-noise ratio inputs. Furthermore, it strikes an optimal balance between reconstruction speed and model accuracy, with a 76.10% improvement in inference speed compared to the DFCAN model.

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来源期刊
Journal of microscopy
Journal of microscopy 工程技术-显微镜技术
CiteScore
4.30
自引率
5.00%
发文量
83
审稿时长
1 months
期刊介绍: The Journal of Microscopy is the oldest journal dedicated to the science of microscopy and the only peer-reviewed publication of the Royal Microscopical Society. It publishes papers that report on the very latest developments in microscopy such as advances in microscopy techniques or novel areas of application. The Journal does not seek to publish routine applications of microscopy or specimen preparation even though the submission may otherwise have a high scientific merit. The scope covers research in the physical and biological sciences and covers imaging methods using light, electrons, X-rays and other radiations as well as atomic force and near field techniques. Interdisciplinary research is welcome. Papers pertaining to microscopy are also welcomed on optical theory, spectroscopy, novel specimen preparation and manipulation methods and image recording, processing and analysis including dynamic analysis of living specimens. Publication types include full papers, hot topic fast tracked communications and review articles. Authors considering submitting a review article should contact the editorial office first.
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