Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

Selection of good particles in cryo-electron micrographs is an important step in the reconstruction of high resolution 3D structures. In this study, we constructed a deep learning-based method to automatically detect particle centers from micrographs. This is a challenging task because of the low signal-to-noise ratio of cryo-EM micrographs, and the size, shape, and grayscale-level differences of particles. We proposed a Fully Convolutional Regression Network (FCRN) that maps the particle image to a continuous distance map that acts like a probability density function of particle centers. This approach is simple, but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experimental results on dataset beta-galactosidase (EMPIAR-10017) [1] showed that FCRN outperfomed Faster-RCNN, Apple picker, and RELION's particle picker. Compared to the ground truth of this dataset, FCRN achieved better picking performance, and 3D structure of those picked particles also had higher resolution.