Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution. We apply this easy and cost-effective Deep-SLAM approach to the anatomical imaging of single neurons in a meso-scale mouse brain, demonstrating its potential for readily converting commonly-used commercialized 2D microscopes to high-throughput 3D imaging, which is previously exclusive for high-end microscopy implementations.

中文翻译：

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深度学习超高分辨率光片附加显微镜（Deep-SLAM），用于对大型生物标本进行简单的各向同性体积成像

大型生物标本的各向同性3D组织学成像是非常需要的，但对当前的荧光显微镜技术仍然具有很高的挑战性。在这里，我们介绍了一种称为深度学习超分辨率光片附加显微镜（Deep-SLAM）的新方法，可以在常规的宽视野显微镜上进行快速，各向同性的光片荧光成像。在集成了将倒置显微镜转换为3D光片显微镜的最小化附加设备之后，我们进一步集成了深度神经网络（DNN）程序，以快速恢复模棱两可的z重构平面，这些平面受光轴向分辨率仍然不足片照明，从而以单细胞分辨率实现厚生物样本的各向同性3D成像。