WikiSysop: Created page with "== Citation == He, L. and Bartesaghi, A. 2026. prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation. Structure. 34, (2026), 1–12. == Abstract == High-throughput data collection in single-particle cryo-electron microscopy (EM) necessitates fast, accurate, and generalizable methods to assess micrograph quality. Manual micrograph curation scales poorly to large datasets and often misclassifies images due to sample-specific variab..."

2026-04-13T12:20:33Z

Created page with "== Citation == He, L. and Bartesaghi, A. 2026. prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation. Structure. 34, (2026), 1–12. == Abstract == High-throughput data collection in single-particle cryo-electron microscopy (EM) necessitates fast, accurate, and generalizable methods to assess micrograph quality. Manual micrograph curation scales poorly to large datasets and often misclassifies images due to sample-specific variab..."

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== Citation ==

He, L. and Bartesaghi, A. 2026. prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation. Structure. 34, (2026), 1–12.

== Abstract ==

High-throughput data collection in single-particle cryo-electron microscopy (EM) necessitates fast, accurate,
and generalizable methods to assess micrograph quality. Manual micrograph curation scales poorly to large
datasets and often misclassifies images due to sample-specific variability. Fully supervised deep-learning
methods show promise in scalability and feature learning. However, dependence on annotated data limits
generalizability. We present prismPYP, a self-supervised, data-driven framework that uses domain-specific
image augmentations to perform label-free feature learning on micrographs and power spectra. From the
learned, low-dimensional image representations, we perform feature-based image clustering that reveals
distinct and consistent indicators of image quality. For validation, we used the resulting high-quality images
to determine high-resolution structures that matched the quality of maps determined using manual curation,
but using fewer particles. prismPYP generalizes across experimental conditions, imaging hardware, and both
conventional single-particle and time-resolved cryo-EM. It is both interpretable and computationally efficient,
and enables rapid, scalable quality assessment for cryo-EM micrographs.

== Keywords ==

== Links ==

https://www.cell.com/structure/fulltext/S0969-2126(26)00057-2

== Related software ==

== Related methods ==

== Comments ==

2026He prismPYP - Revision history