Physics-based Deep Learning for Imaging Neuronal Activity via Two-photon and Light Field Microscopy
Physics-based Deep Learning for Imaging Neuronal Activity via Two-photon and Light Field Microscopy
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基于物理的深度学习通过双光子和光场显微镜对神经元活动进行成像
DOI：
10.1101/2022.10.11.511633
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Verinaz-Jadan H
中科院分区：
文献类型：
--
作者：
Verinaz-Jadan H
光场显微镜（LFM）是一种成像技术，由于其3D成像速度，为研究生物系统中的快速动力学提供了机会，对功能性神经成像特别有吸引力。传统的基于模型的方法在显微镜中用于从光场数据重建三维图像，受到重建伪影的影响，并且计算要求很高。这项工作为LFM引入了一个深度神经网络，用于在不利条件下成像神经元活动：有限的训练数据、背景噪声和分散的哺乳动物脑组织。网络的结构是通过展开ISTA算法得到的，并且是基于观察到组织中的神经元是稀疏的。我们的方法也是基于成像系统的一种新型建模，该系统使用线性卷积神经网络来适应采集过程的物理特性。我们以一种基于对抗性训练框架的半监督方式训练网络。训练所需的小标记数据集是通过双光子显微镜从单个样本中获得的，双光子显微镜是一种点扫描3D成像技术，可实现高空间分辨率和深层组织穿透，但速度低于LFM。我们在网络架构的设计和训练过程中引入了系统的物理知识，以完成我们的半监督方法。实验表明，在该场景下，考虑到重建质量、对功能成像的泛化和重建速度，我们的方法在通过LFM成像哺乳动物脑组织神经元活动方面表现优于典型的深度学习和基于模型的重建策略。
Light Field Microscopy (LFM) is an imaging technique that offers the opportunity to study fast dynamics in biological systems due to its 3D imaging speed and is particularly attractive for functional neuroimaging. Traditional model-based approaches employed in microscopy for reconstructing 3D images from light-field data are affected by reconstruction artifacts and are computationally demanding. This work introduces a deep neural network for LFM to image neuronal activity under adverse conditions: limited training data, background noise, and scattering mammalian brain tissue. The architecture of the network is obtained by unfolding the ISTA algorithm and is based on the observation that neurons in the tissue are sparse. Our approach is also based on a novel modelling of the imaging system that uses a linear convolutional neural network to fit the physics of the acquisition process. We train the network in a semi-supervised manner based on an adversarial training framework. The small labelled dataset required for training is acquired from a single sample via two-photon microscopy, a point-scanning 3D imaging technique that achieves high spatial resolution and deep tissue penetration but at a lower speed than LFM. We introduce physics knowledge of the system in the design of the network architecture and during training to complete our semi-supervised approach. We experimentally show that in the proposed scenario, our method performs better than typical deep learning and model-based reconstruction strategies for imaging neuronal activity in mammalian brain tissue via LFM, considering reconstruction quality, generalization to functional imaging, and reconstruction speed.
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