Focus prediction in digital holographic microscopy using deep convolutional neural networks

Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its potential in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of Madin-Darby canine kidney cell clusters encoded in digital holograms. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, without performing numerical propagation. This paper reports on extensions to preliminary work published earlier as one of the first applications of deep learning in the field of digital holographic microscopy.