Zhang Hongdong, Xu Zhiqiang, Chen Ningbo, Ma Fei, Zheng Wei, Liu Chengbo, Meng Jing
School of Computer, Qufu Normal University, Rizhao 276826, China.
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Biomed Opt Express. 2024 Jun 17;15(7):4300-4317. doi: 10.1364/BOE.527919. eCollection 2024 Jul 1.
Calcium imaging is susceptible to motion distortions and background noises, particularly for monitoring active animals under low-dose laser irradiation, and hence unavoidably hinder the critical analysis of neural functions. Current research efforts tend to focus on either denoising or dewarping and do not provide effective methods for videos distorted by both noises and motion artifacts simultaneously. We found that when a self-supervised denoising model of DeepCAD [Nat. Methods18, 1359 (2021)10.1038/s41592-021-01225-0] is used on the calcium imaging contaminated by noise and motion warping, it can remove the motion artifacts effectively but with regenerated noises. To address this issue, we develop a two-level deep-learning (DL) pipeline to dewarp and denoise the calcium imaging video sequentially. The pipeline consists of two 3D self-supervised DL models that do not require warp-free and high signal-to-noise ratio (SNR) observations for network optimization. Specifically, a high-frequency enhancement block is presented in the denoising network to restore more structure information in the denoising process; a hierarchical perception module and a multi-scale attention module are designed in the dewarping network to tackle distortions of various sizes. Experiments conducted on seven videos from two-photon and confocal imaging systems demonstrate that our two-level DL pipeline can restore high-clarity neuron images distorted by both motion warping and background noises. Compared to typical DeepCAD, our denoising model achieves a significant improvement of approximately 30% in image resolution and up to 28% in signal-to-noise ratio; compared to traditional dewarping and denoising methods, our proposed pipeline network recovers more neurons, enhancing signal fidelity and improving data correlation among frames by 35% and 60% respectively. This work may provide an attractive method for long-term neural activity monitoring in awake animals and also facilitate functional analysis of neural circuits.
钙成像容易受到运动失真和背景噪声的影响，特别是在低剂量激光照射下监测活动的动物时，因此不可避免地阻碍了对神经功能的关键分析。目前的研究工作往往集中在去噪或去扭曲上，并没有为同时受到噪声和运动伪影影响而失真的视频提供有效的方法。我们发现，当将DeepCAD [《自然·方法》18, 1359 (2021)10.1038/s41592-021-01225-0] 的自监督去噪模型用于受噪声和运动扭曲污染的钙成像时，它可以有效去除运动伪影，但会产生再生噪声。为了解决这个问题，我们开发了一个两级深度学习（DL）管道，用于依次对钙成像视频进行去扭曲和去噪。该管道由两个3D自监督DL模型组成，它们在网络优化时不需要无扭曲和高信噪比（SNR）的观测数据。具体来说，在去噪网络中提出了一个高频增强块，以在去噪过程中恢复更多的结构信息；在去扭曲网络中设计了一个分层感知模块和一个多尺度注意力模块，以处理各种大小的失真。对来自双光子和共聚焦成像系统的七个视频进行的实验表明，我们的两级DL管道可以恢复因运动扭曲和背景噪声而失真的高清晰度神经元图像。与典型的DeepCAD相比，我们的去噪模型在图像分辨率上有大约30%的显著提高，在信噪比上提高了28%；与传统的去扭曲和去噪方法相比，我们提出的管道网络恢复了更多的神经元，分别将信号保真度提高了35%，将帧间数据相关性提高了60%。这项工作可能为清醒动物的长期神经活动监测提供一种有吸引力的方法，也有助于神经回路的功能分析。
