3DEM-Methods - Recent changes [en]

2025Vivas iceFinder

Vilas — Sat, 26 Sep 2026 15:37:11 GMT

Created page with "== Citation == A. Vivas-Lago, D. Castaño-Díez, Few-shot learning for non-vitrified ice segmentation, Scientific Reports, 15, 1, 5501, (2025). == Abstract == This study introduces Ice Finder, a novel tool for quantifying crystalline ice in cryo-electron tomography, addressing a critical gap in existing methodologies. We present the first application of the meta-learning paradigm to this field, demonstrating that diverse tomographic tasks across datasets can be unified..."

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== Citation ==
A. Vivas-Lago, D. Castaño-Díez, Few-shot learning for non-vitrified ice segmentation, Scientific Reports, 15, 1, 5501, (2025).

== Abstract ==
This study introduces Ice Finder, a novel tool for quantifying crystalline ice in cryo-electron tomography, addressing a critical gap in existing methodologies. We present the first application of the meta-learning paradigm to this field, demonstrating that diverse tomographic tasks across datasets can be unified under a single meta-learning framework. By leveraging few-shot learning, our approach enhances domain generalization and adaptability to domain shifts, enabling rapid adaptation to new datasets with minimal examples. Ice Finder's performance is evaluated on a comprehensive set of in situ datasets from EMPIAR, showcasing its ease of use, fast processing capabilities, and millisecond inference times.

== Keywords ==
cryo-electron tomography, non-vitrified ice segmentation, crystalline ice, few-shot learning, meta-learning, Ice Finder,

== Links ==
https://doi.org/10.1038/s41598-025-86308-0

Main Page

Vilas — Sat, 26 Sep 2026 15:36:27 GMT

Image preprocessing

← Older revision		Revision as of 15:36, 26 September 2026
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	\| [[2018Wu_contrast]]		\| [[2018Wu_contrast]]
	\| Contrast enhancement to improve alignability		\| Contrast enhancement to improve alignability
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			\| Paper
			\| [[2025Vivas_iceFinder]]
			\| Quantifying the existence of crystalline ice in images
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2026Poudel CryoFSL

WikiSysop — Thu, 24 Sep 2026 05:19:54 GMT

Created page with "== Citation == Poudel, B., Gyawali, R., Dhakal, A., Cheng, J. and Xu, D. 2026. CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs. Briefings in Bioinformatics. 27, 3 (2026), bbag285. == Abstract == Accurate identification of protein particles in cryo-electron microscopy (cryo-EM) micrographs is crucial for high-resolution structure determination, but remains challenging due to the h..."

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== Citation ==

Poudel, B., Gyawali, R., Dhakal, A., Cheng, J. and Xu, D. 2026. CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs. Briefings in Bioinformatics. 27, 3 (2026), bbag285.

== Abstract ==

Accurate identification of protein particles in cryo-electron microscopy (cryo-EM) micrographs is crucial for high-resolution structure determination, but remains challenging due to the heavy reliance on extensive annotated datasets and the difficulty of ensuring robustness under low signal-to-noise ratio (SNR) conditions. Current approaches require large annotations and exhibit poor generalization to new protein targets. We present CryoFSL (Cryo-EM Few Shot-Learning), a novel few-shot learning framework built on Segment Anything Model 2 with lightweight adapters, enabling robust particle picking with as few as five labeled micrographs and significantly reducing the annotation burden. The framework’s hierarchical adapter design supports dynamic feature modulation for low-SNR and heterogeneous conditions, resolving the trade-off between annotation burden and performance. CryoFSL surpasses both traditional template-based methods and state-of-the-art deep learning models across diverse proteins in the few-shot learning setting, achieving superior recall, precision, and 3D reconstruction resolution with minimal supervision. It maintains stability across heterogeneous micrographs and consistently detects high-quality particles with fewer false-positives. Notably, CryoFSL achieves competitive resolution in density map reconstruction with just a fraction of the particles picked by other methods, redefining efficiency and quality in cryo-EM analysis. This work paves the way for scalable, generalizable, and annotation-efficient particle-picking pipelines. The code is available at https://github.com/biplabpoudel25/CryoFSL.

== Keywords ==

== Links ==

https://academic.oup.com/bib/article/27/3/bbag285/8703117

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Thu, 24 Sep 2026 05:18:52 GMT

Automatic particle picking

← Older revision		Revision as of 05:18, 24 September 2026
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	\| [[2026He_prismPYP]]		\| [[2026He_prismPYP]]
	\| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation		\| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
			\|-

			\| Paper
			\| [[2026Poudel_CryoFSL]]
			\| CryoFSL: Few Shot Learning for particle picking
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2021Chen Ewald

WikiSysop — Thu, 24 Sep 2026 05:02:21 GMT

Created page with "== Citation == Chen, J.P., Schmidt, K.E., Spence, J.C. and Kirian, R.A. 2021. A new solution to the curved Ewald sphere problem for 3D image reconstruction in electron microscopy. Ultramicroscopy. 224, (2021), 113234. == Abstract == We develop an algorithm capable of imaging a three-dimensional object given a collection of two-dimensional images of that object that are significantly influenced by the curvature of the Ewald sphere. These two-dimensional images cannot b..."

New page

== Citation ==

Chen, J.P., Schmidt, K.E., Spence, J.C. and Kirian, R.A. 2021. A new solution to the curved Ewald sphere problem for 3D image reconstruction in electron microscopy. Ultramicroscopy. 224, (2021), 113234.

== Abstract ==

We develop an algorithm capable of imaging a three-dimensional object given a collection of two-dimensional images of that object that are significantly influenced
by the curvature of the Ewald sphere. These two-dimensional images cannot be approximated as projections of the object. Such an algorithm is useful in cryoelectron
microscopy where larger samples, higher resolution, or lower energy electron beams are desired, all of which contribute to the significance of Ewald
curvature.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S0304399121000292

== Related software ==

== Related methods ==

== Comments ==

Main Page

WikiSysop — Thu, 24 Sep 2026 05:01:51 GMT

3D Reconstruction

← Older revision		Revision as of 05:01, 24 September 2026
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	\| [[2021Abrishami_Localized]]		\| [[2021Abrishami_Localized]]
	\| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data		\| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
			\|-

			\| Paper
			\| [[2021Chen_Ewald]]
			\| 3D Reconstruction considering the Ewald sphere
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