DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS.

ABSTRACT: Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules o

SUBMITTER: Liu C 

PROVIDER: S-EPMC10552869 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature