论文标题
使用神经网络的神经元的计算套管显微镜
Computational Cannula Microscopy of neurons using Neural Networks
论文作者
论文摘要
计算套管显微镜是一种微创成像技术，可以使高分辨率成像内部组织内部。在这里，我们应用人工神经网络来实现更有效地扩展到较大视野的快速，发电的图像重建。具体而言，我们证明了培养神经元和荧光珠的广阔荧光显微镜，其视野为200 $ $ $ m（直径）（直径），并且使用仅220 $μ$ m的套管的套管，分辨率小于10 $ $ m。此外，我们表明这种方法也可以扩展到宏观摄影。
Computational Cannula Microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable fast, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with field of view of 200$μ$m (diameter) and resolution of less than 10$μ$m using a cannula of diameter of only 220$μ$m. In addition, we show that this approach can also be extended to macro-photography.
