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深度学习光场显微镜在生物动力学 3D 成像中的实用指南。

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics.

机构信息

School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

出版信息

STAR Protoc. 2023 Mar 17;4(1):102078. doi: 10.1016/j.xpro.2023.102078. Epub 2023 Jan 29.

Abstract

Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capable of capturing optically encoded dynamic signals. Then, we detail the data processing and model training of a view-channel-depth (VCD) neural network, which enables instant 3D image reconstruction from a single 2D light-field snapshot. Finally, we describe VCD-LFM imaging of several model organisms and demonstrate image-based quantitative studies on neural activities and cardio-hemodynamics. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021)..

摘要

在这里,我们提供了一个逐步的协议,用于实现深度学习增强的光场显微镜,从而实现对瞬时生物过程的 3D 成像。我们首先提供了构建能够捕获光编码动态信号的光场显微镜(LFM)的说明。然后,我们详细说明了视图通道深度(VCD)神经网络的数据处理和模型训练过程,该网络可从单个 2D 光场快照中实现即时的 3D 图像重建。最后,我们描述了几种模式生物的 VCD-LFM 成像,并展示了基于图像的神经活动和心肺动力学的定量研究。有关此协议的使用和执行的完整详细信息,请参阅 Wang 等人(2021)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/638f/9898296/1a6c93b6d098/fx1.jpg

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