PT - JOURNAL ARTICLE AU - Pinkard, Henry AU - Phillips, Zachary AU - Babakhani, Arman AU - Fletcher, Daniel A. AU - Waller, Laura TI - Single-shot autofocus microscopy using deep learning AID - 10.1101/587485 DP - 2019 Jan 01 TA - bioRxiv PG - 587485 4099 - http://biorxiv.org/content/early/2019/03/23/587485.short 4100 - http://biorxiv.org/content/early/2019/03/23/587485.full AB - Maintaining an in-focus image over long time scales is an essential and non-trivial task for a variety of microscopic imaging applications. Here, we present an autofocusing method that is inexpensive, fast, and robust. It requires only the addition of one or a few off-axis LEDs to a conventional transmitted light microscope. Defocus distance can be estimated and corrected based on a single image under this LED illumination using a neural network that is small enough to be trained on a desktop CPU in a few hours. In this work, we detail the procedure for generating data and training such a network, explore practical limits, and describe relevant design principles governing the illumination source and network architecture.