小柯机器人

美国国立卫生研究院Jiji Chen等研究人员合作开发出去噪和锐化荧光显微镜图像体积的新方法。2021年5月31日，国际知名学术期刊《自然—方法学》在线发表了这一成果。

研究人员报道了名为RCAN（residual channel attention networks）的方法，可用于恢复和增强体积延时（四维）荧光显微镜数据。首先，研究人员修改了RCAN来处理图像体积，并表明这个网络的去噪能力能够与其他三个最先进的神经网络相媲美。研究人员使用了RCAN来恢复嘈杂的四维超分辨率数据，从而实现了数万图像的图像捕获（数千体积），而没有明显的光漂白。

其次，使用该模拟，研究人员发现，RCAN使得能够分辨率增强等同于或更优于其他网络。第三，研究人员利用RCAN进行了共聚焦显微镜的去噪和分辨率改善，这实现了约2.5倍的横向分辨率增强。第四，通过将膨胀显微镜数据作为基准，研究人员开发了方法来提高结构光照明显微镜的空间分辨率，并实现横向约1.9倍、轴向约为3.6倍的改善。最后，研究人员描述了去噪和解决的限制，为评估和进一步提高网络性能提供了实际基准。

附：英文原文

Title: Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

Author: Jiji Chen, Hideki Sasaki, Hoyin Lai, Yijun Su, Jiamin Liu, Yicong Wu, Alexander Zhovmer, Christian A. Combs, Ivan Rey-Suarez, Hung-Yu Chang, Chi Chou Huang, Xuesong Li, Min Guo, Srineil Nizambad, Arpita Upadhyaya, Shih-Jong J. Lee, Luciano A. G. Lucas, Hari Shroff

Issue&Volume: 2021-05-31

Abstract: We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance. Three-dimensional residual channel attention networks (RCAN) enable improved image denoising and resolution enhancement on volumetric time-lapse fluorescence microscopy data, allowing longitudinal super-resolution imaging of living samples.

DOI: 10.1038/s41592-021-01155-x

Source: https://www.nature.com/articles/s41592-021-01155-x

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex