WikiSysop: Created page with "== Citation == D. Van Veen et al., “Missing wedge completion via unsupervised learning with coordinate networks,” Intl. J. Molecular Sciences, vol. 25, no. 10, p. 5473, 2024. == 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 qua..."

2024-10-18T05:55:17Z

Created page with "== Citation == D. Van Veen et al., “Missing wedge completion via unsupervised learning with coordinate networks,” Intl. J. Molecular Sciences, vol. 25, no. 10, p. 5473, 2024. == 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 qua..."

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== Citation ==

D. Van Veen et al., “Missing wedge completion via unsupervised learning with coordinate networks,” Intl. J. Molecular Sciences, vol. 25, no. 10, p. 5473, 2024.

== 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.

== Keywords ==

== Links ==

https://www.mdpi.com/1422-0067/25/10/5473

== Related software ==

== Related methods ==

== Comments ==

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