Authors

Dave Van Veen
Jesús G Galaz-Montoya
Liyue Shen
Philip Baldwin
Akshay S Chaudhari
Dmitry Lyumkis
Michael F Schmid
Wah Chiu
John Pauly

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

Publication Date

5-17-2024

Journal

International Journal of Molecular Sciences

DOI

10.3390/ijms25105473

PMID

38791508

PMCID

PMC11121946

PubMedCentral® Posted Date

5-17-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Unsupervised Machine Learning, Neural Networks, Computer, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Electron Microscope Tomography, Cryoelectron Microscopy, Algorithms, Deep Learning, machine learning, artificial intelligence, coordinate networks, unsupervised learning, missing wedge, cryogenic electron tomography (cryoET), cryogenic electron microscopy (cryoEM), reconstruction, simulation

Abstract

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3-20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

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