Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms

TL;DR

This paper introduces a novel 3D deep learning model for supervised segmentation of macromolecules in Electron Cryo-Subtomograms, significantly improving accuracy and generalization over existing methods.

Contribution

The study presents a new 3D convolutional neural network architecture inspired by FCN and Encoder-Decoder models for macromolecular segmentation in CECT images.

Findings

Significantly improved segmentation performance on simulated data

Model generalizes to unseen structures

Outperforms baseline approaches in accuracy

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 of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data.