Figure 2.

Overview of DNN analysis pipeline. (A) DHPSF training images are generated from the pupil function of our microscope using scalar diffraction theory. A uniform background is added, and noise is applied to model the shot noise and readout noise of real images. We refer to the network trained on these DHPSF-only images as UBNet. (B) Images designed to train the network to handle HIV-1 Gag cell data are created by adding CB noise images with sharp edges (which already includes shot noise but not readout noise as described in materials and methods) to DHPSF images as described in (A). We refer to the network trained on these images as CBNet. (C) The analysis pipeline takes full-size images from the microscope (left panel) and recognizes individual DHPSF objects (middle panel). Regions recognized in the first step are cropped and passed to the second stage, which returns x, y, z, and intensity estimates for each region identified in the first stage.