Automated Removal of Corrupted Tilts in Cryo-Electron Tomography

Abstract

Cryo-electron tomography (cryo-ET) enables the visualization of macromolecular structures in their near-native cellular environment. However, acquired tilt series are often compromised by image corruption due to drift, contamination, and ice reflections. Manually identifying and removing corrupted tilts is subjective and time-consuming, making an automated approach necessary. In this study, we present a deep learning-based method for automatically removing corrupted tilts. We evaluated 13 different neural network architectures, including convolutional neural networks (CNNs) and transformers. Using a dataset of 435 annotated tilt series, we trained models for both binary and multiclass classification of corrupted tilts. We demonstrate the high efficiency and reliability of these automated approaches for removing corrupted tilts in cryo-ET and provide a framework, including models trained on cryo-ET data, that allows users to apply these models directly to their tilt series, improving the quality and consistency of downstream cryo-ET data processing.