Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy
Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy
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DOI：
10.1364/boe.427099
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发表时间：
2021-08-01
影响因子：
3.4
通讯作者：
Greenabum, Alon
中科院分区：
文献类型：
--
作者：
Li, Chen;Moatti, Adele;Greenabum, Alon
光片荧光显微镜（LSFM）是一种微创和高通量成像技术，非常适合以亚细胞分辨率捕获大量组织。LSFM的一个基本要求是在样品中激发一个选择平面的光片的无缝重叠，与物镜的焦平面。然而，在标本的折射率的空间异质性往往导致违反这一要求时，成像的深层组织。为了解决这个问题，通常使用自动对焦方法来重新聚焦物镜在光片上的焦平面。然而，自动对焦技术是缓慢的，因为它们需要捕获一堆图像，并且在存在主导体成像的球差时往往会失败。为了解决这些问题，我们提出了一个基于深度学习的自动对焦框架，该框架可以根据两张散焦图像估计物镜焦平面相对于光片的位置。该方法分别在小块和大块图像上优于传统的最佳自动对焦方法或提供与之相当的结果。当训练的网络与定制的LSFM集成时，使用确定性度量来进一步改进网络的预测。在清除基因标记的小鼠前脑和猪耳蜗样本上实时验证了该网络的性能。我们的研究提供了一个框架，可以改进光片显微镜及其在高空间分辨率的大型3D标本成像中的应用。(c)根据OSA开放获取出版协议的条款，2021年美国光学学会
Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens. However, spatial heterogeneity in the refractive index of the specimen often results in violation of this requirement when imaging deep in the tissue. To address this issue, autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet. Yet, autofocus techniques are slow since they require capturing a stack of images and tend to fail in the presence of spherical aberrations that dominate volume imaging. To address these issues, we present a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images. This approach outperforms or provides comparable results with the best traditional autofocus method on small and large image patches respectively. When the trained network is integrated with a custom-built LSFM, a certainty measure is used to further refine the network's prediction. The network performance is demonstrated in real-time on cleared genetically labeled mouse forebrain and pig cochleae samples. Our study provides a framework that could improve light-sheet microscopy and its application toward imaging large 3D specimens with high spatial resolution. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement