Autofocusing is a critical step for high-quality microscopic imaging of specimens, especially for measurements that extend over time covering large fields of view. Autofocusing is generally practiced using two main approaches. Hardware-based optical autofocusing methods rely on additional distance sensors that are integrated with a microscopy system. Algorithmic autofocusing methods, on the other hand, regularly require axial scanning through the sample volume, leading to longer imaging times, which might also introduce phototoxicity and photobleaching on the sample. Here, we demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane. We illustrate the efficacy of Deep-R using various tissue sections that were imaged using fluorescence and brightfield microscopy modalities and demonstrate snapshot autofocusing under different scenarios, such as a uniform axial defocus as well as a sample tilt within the field-of-view. Our results reveal that Deep-R is significantly faster when compared with standard online algorithmic autofocusing methods. This deep learning-based blind autofocusing framework opens up new opportunities for rapid microscopic imaging of large sample areas, also reducing the photon dose on the sample.

中文翻译：

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使用深度学习对显微镜图像进行单次自动聚焦

自动聚焦是对标本进行高质量显微成像的关键步骤，特别是对于随时间扩展而覆盖大视野的测量。通常使用两种主要方法来实现自动对焦。基于硬件的光学自动聚焦方法依赖于与显微镜系统集成的其他距离传感器。另一方面，算法自动聚焦方法通常需要轴向扫描整个样品体积，从而导致更长的成像时间，这也可能对样品造成光毒性和光漂白。在这里，我们演示了一种基于深度学习的离线自动聚焦方法，称为Deep-R，该方法经过训练可以快速，盲目地自动聚焦在任意离焦平面上获取的标本的单次显微图像。我们通过使用荧光和明场显微镜方法对各种组织切片进行成像来说明Deep-R的功效，并展示了在不同情况下的快照自动对焦，例如均匀的轴向散焦以及视野内的样品倾斜。我们的结果表明，与标准的在线算法自动对焦方法相比，Deep-R的速度明显更快。这种基于深度学习的盲自动聚焦框架为大型样品区域的快速显微成像打开了新的机遇，也减少了样品上的光子剂量。我们的结果表明，与标准的在线算法自动对焦方法相比，Deep-R的速度明显更快。这种基于深度学习的盲自动聚焦框架为大型样品区域的快速显微成像打开了新的机遇，也减少了样品上的光子剂量。我们的结果表明，与标准的在线算法自动对焦方法相比，Deep-R的速度明显更快。这种基于深度学习的盲自动聚焦框架为大型样品区域的快速显微成像打开了新的机遇，也减少了样品上的光子剂量。