论文标题
3D计算套管荧光显微镜由人工神经网络启用
3D Computational Cannula Fluorescence Microscopy enabled by Artificial Neural Networks
论文作者
论文摘要
计算套管显微镜（CCM）是在组织内部深处的高分辨率广场荧光成像方法，它的侵入性最小。手术套管不是使用常规透镜，而是充当激发和荧光发射的光管，其中计算方法用于图像可视化。在这里，我们使用人工神经网络增强了CCM，以实现培养的神经元和荧光珠的3D成像，后者是体积幻像内的。我们在实验上证明了〜6UM的横向分辨率，视场〜200UM和〜50UM的轴向切片，深度降至〜700UM，所有这些都以〜3ms/帧的计算时间在笔记本电脑计算机上实现。
Computational Cannula Microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of ~6um, field of view ~200um and axial sectioning of ~50um for depths down to ~700um, all achieved with computation time of ~3ms/frame on a laptop computer.
