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深度学习提升光场成像质量
作者:小柯机器人 发布时间:2021/5/13 15:59:13

德国欧洲分子生物学实验室Anna Kreshuk、Robert Prevedel等研究人员合作利用深度学习提升光场成像质量。该研究于2021年5月7日发表于国际一流学术期刊《自然—方法学》。

研究人员提出了一种人工智能增强显微镜的框架,将混合光场光片显微镜和基于深度学习的体积重建相结合。在这个方法中,伴随采集的高分辨率二维光片图像连续被用作训练数据,以及卷积神经网络重构原始LFM数据的验证。这个网络以视频速率处理量提供了高质量的三维重建,并能够基于高分辨率的光片图像进一步完善。研究人员通过以高达100 Hz的体积成像速率对青鳉心脏动力学和斑马鱼神经活动进行了成像,从而证明了该方法的效率。

据介绍,对于大型生命科学领域中的许多应用而言,高速可视化动态过程在大型三维视野中至关重要。光场显微镜(LFM)已成为一种快速获取体积图像的工具,但由于计算量大且易于伪影的图像重建过程,其有效通量和在生物学中的广泛应用受到了阻碍。

附:英文原文

Title: Deep learning-enhanced light-field imaging with continuous validation

Author: Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Juan Carlos Boffi, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel, Anna Kreshuk

Issue&Volume: 2021-05-07

Abstract: Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence–enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning–based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100Hz.

DOI: 10.1038/s41592-021-01136-0

Source: https://www.nature.com/articles/s41592-021-01136-0

期刊信息

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:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex