Speaker
Description
Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens. However, in a common Cryo-EM processing workflow, the 3D alignment step, due to the very low signal-to-noise ratio of Cryo-EM images, is a prone error process. Thus, the reconstructed 3D maps can show areas with low resolution.
In this work, a novel method to align sets of projection images in the 3D space is presented. Our proposal is based on deep learning networks. Specifically, we propose to design several deep networks on a regionalized basis, creating a bank of networks to classify the projection images in sub-regions of the 3D space and, then, making a refinement of the final 3D alignment parameters. We show that the method applied to experimental data results in accurately aligned images.
