  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.
