  To obtain microscope images at multiple focal planes, the distance between
the objective and sample can be mechanically adjusted. Images are acquired
sequentially at each axial distance. Digital refocusing with a light-emitting
diode (LED) array microscope allows elimination of this mechanical movement. In
an LED array microscope, the light source of a conventional widefield
microscope is replaced with a 2-dimensional LED matrix. A stack of images is
acquired from the LED array microscope by sequentially illuminating each LED
and capturing an image. Previous work has shown that we can achieve digital
refocusing by post-processing this LED image stack. Though mechanical scanning
is eliminated, digital refocusing with an LED array microscope has low temporal
resolution due to the acquisition of multiple images. In this work, we propose
a new paradigm for multi-focal plane microscopy for live imaging, utilizing an
LED array microscope and deep learning. In our deep learning approach, we look
for a single LED illumination pattern that allows the information from multiple
focal planes to be multiplexed into a single image. We jointly optimize this
LED illumination pattern with the parameters of a post-processing deep neural
network, using a training set of LED image stacks from fixed, not live, Dugesia
japonica planarians. Once training is complete, we obtain multiple focal planes
by inputting a single multiplexed LED image into the trained post-processing
deep neural network. We demonstrate live imaging of a D. japonica planarian at
5 focal planes with our method.
