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..."

New page

== 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
			\|-

			\| Paper
			\| [[2025Vivas_iceFinder]]
			\| Quantifying the existence of crystalline ice in images
	\|-		\|-

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..."

New page

== 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 ==

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

Automatic particle picking

← Older revision		Revision as of 05:18, 24 September 2026
Line 1,851:		Line 1,851:
	\| [[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
	\|-		\|-

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
	\|-		\|-

2026Burton PASR

WikiSysop — Fri, 18 Sep 2026 05:08:06 GMT

Created page with "== Citation == Burton Smith, R. and Murata, K. 2026. Post-acquisition super resolution for cryo-electron microscopy. IUCrJ. 13, 5 (2026). == Abstract == Recently, reports have demonstrated achieving resolutions beyond the physical Nyquist limit using super resolution acquisition. Here, we demonstrate exceeding this limitation by pre-processing the raw micrograph movies from counting mode data that have already reached the physical Nyquist reconstruction resolution. To..."

New page

== Citation ==

Burton Smith, R. and Murata, K. 2026. Post-acquisition super resolution for cryo-electron microscopy. IUCrJ. 13, 5 (2026).

== Abstract ==

Recently, reports have demonstrated achieving resolutions beyond the physical
Nyquist limit using super resolution acquisition. Here, we demonstrate
exceeding this limitation by pre-processing the raw micrograph movies from
counting mode data that have already reached the physical Nyquist reconstruction
resolution. To demonstrate functionality, micrograph movies of
multiple datasets were pre-processed, showing that it is possible to exceed the
physical Nyquist limit via pixel doubling before motion correction. We call this
‘post-acquisition super resolution’, or PASR. While this was originally developed
for processing of giant virus datasets, where acquiring at high magnification
is not always possible or desirable, it is also shown to work for smaller
objects such as adeno-associated virus and apoferritin, both of which are high
symmetry, and jack bean urease, with lower symmetry. PASR reduces the
magnification required to achieve desired resolutions, which may increase
collection efficiency. Further, PASR may be applied on top of acquisition-time
super resolution data collection, allowing results to exceed not just the physical
sampling limit, but also the super resolution sampling limit. However, to benefit
from PASR, data must already reach the Nyquist limit.

== Keywords ==

== Links ==

https://journals.iucr.org/m/issues/2026/05/00/rq5016/index.html

== Related software ==

== Related methods ==

== Comments ==

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WikiSysop — Fri, 18 Sep 2026 05:06:50 GMT

2D Preprocessing

← Older revision		Revision as of 05:06, 18 September 2026
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	\| [[2025Starynska_Membrane]]		\| [[2025Starynska_Membrane]]
	\| Membrane detection and substraction from micrographs		\| Membrane detection and substraction from micrographs
			\|-

			\| Paper
			\| [[2026Burton_PASR]]
			\| Post-acquisition Super Resolution
	\|-		\|-

2026Klaholz Concepts

WikiSysop — Thu, 17 Sep 2026 05:54:07 GMT

Created page with "== Citation == Klaholz, B.P. 2026. A discussion of cryo-EM terminology as the outreach and number of PDB entries expand. IUCrJ. 13, 5 (2026). == Abstract == Cryo electron microscopy (cryo-EM) has made great advances in the last decade, progressively increasing its impact in structural biology as a key method to address molecular structures and mechanisms of various macromolecular complexes. Single-particle cryo-EM will soon equal the number of yearly entries in the Pr..."

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== Citation ==

Klaholz, B.P. 2026. A discussion of cryo-EM terminology as the outreach and number of PDB entries expand. IUCrJ. 13, 5 (2026).

== Abstract ==

Cryo electron microscopy (cryo-EM) has made great advances in the last
decade, progressively increasing its impact in structural biology as a key method
to address molecular structures and mechanisms of various macromolecular
complexes. Single-particle cryo-EM will soon equal the number of yearly entries
in the Protein Data Bank from structures determined by X-ray crystallography.
This is largely thanks to improved cryo electron microscope instrumentation and
advanced image-processing tools and structure-sorting methods. As the role of
cryo-EM is expanding to an increasingly large community, including newcomers
and scientists joining from related fields, it is timely to revisit some fundamental
concepts and basics of single-particle cryo-EM and image processing as terminology
has become less well defined and, in some cases, confusing. Here we
summarize and define some typical terms important for understanding the
underlying physical concepts. These include ‘cryo-EM’, ‘cryo-ET’, ‘3D reconstruction’,
‘coarsening’, ‘contour level’ and others. We also discuss resolution
estimation and map deposition.

== Keywords ==

== Links ==

https://journals.iucr.org/m/issues/2026/05/00/hen5001/index.html

== Related software ==

== Related methods ==

== Comments ==

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WikiSysop — Thu, 17 Sep 2026 05:53:25 GMT

Books and reviews

← Older revision		Revision as of 05:53, 17 September 2026
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	\| [[2026Gauvin_200kV]]		\| [[2026Gauvin_200kV]]
	\| Example of a 200kV CryoEM facility and discussion of its cost		\| Example of a 200kV CryoEM facility and discussion of its cost
			\|-

			\| Paper
			\| [[2026Klaholz_Concepts]]
			\| Review of cryoEM related concepts
	\|-		\|-

2025Xu CryoDataBot

WikiSysop — Tue, 15 Sep 2026 09:15:35 GMT

Created page with "== Citation == Xu, Q., Wu, L., Rebelo, M., Feng, S., Yu, X., Farheen, F., Kihara, D. and Zhou, Z.H. 2025. CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology. GigaScience. 14, (2025), giaf127. == Abstract == Cryogenic electron microscopy (cryoEM) has revolutionized structural biology by enabling atomic-resolution visualization of biomacromolecules. With artificial intelligence (AI) increasing role in newly developed cryoEM tools, task-sp..."

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== Citation ==

Xu, Q., Wu, L., Rebelo, M., Feng, S., Yu, X., Farheen, F., Kihara, D. and Zhou, Z.H. 2025. CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology. GigaScience. 14, (2025), giaf127.

== Abstract ==

Cryogenic electron microscopy (cryoEM) has revolutionized structural biology by enabling atomic-resolution visualization of biomacromolecules. With artificial intelligence (AI) increasing role in newly developed cryoEM tools, task-specific datasets have become essential. Yet assembling such datasets often demands considerable effort and domain expertise, constraining AI-driven cryoEM tool development efforts. Here, we present CryoDataBot, an automated pipeline that addresses this gap. CryoDataBot streamlines data retrieval, preprocessing, and labeling, with fine-grained quality control and flexible customization, enabling efficient generation of robust datasets. CryoDataBot’s effectiveness is demonstrated through improved training efficiency in U-Net models and rapid, effective retraining of CryoREAD, a widely used RNA modeling tool. By simplifying the workflow and offering customizable quality control, CryoDataBot enables researchers to easily tailor dataset construction to the specific objectives of their models, while ensuring high data quality and reducing manual workload. This flexibility supports tools development for a wide range of applications in AI-driven structural biology.

== Keywords ==

== Links ==

https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf127/8297133

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== Related methods ==

== Comments ==

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WikiSysop — Tue, 15 Sep 2026 09:14:46 GMT

Databases

← Older revision		Revision as of 09:14, 15 September 2026
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	\| [[2025Giri_Sharpening]]		\| [[2025Giri_Sharpening]]
	\| A labeled dataset for map enhancement		\| A labeled dataset for map enhancement
			\|-

			\| Paper
			\| [[2025Xu_CryoDataBot]]
			\| Automatic construction of map datasets
	\|-		\|-

2026Sun QwenCryoMarker

WikiSysop — Thu, 03 Sep 2026 05:57:51 GMT

Created page with "== Citation == Sun, Y., Zhao, J., Xu, N., Wang, L., Ding, W. and Li, M. 2026. QwenCryoMarker: a universal post-processing framework for contamination-aware particle cleaning. Acta Crystallographica Sec. D. 82, 9 (2026). == Abstract == Cryo-electron microscopy (cryo-EM) micrographs are frequently contaminated by carbon edges, ice crystals, ethane bubbles and other high-contrast artifacts. These contaminants trigger abundant false positives in automated particle pickers..."

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== Citation ==

Sun, Y., Zhao, J., Xu, N., Wang, L., Ding, W. and Li, M. 2026. QwenCryoMarker: a universal post-processing framework for contamination-aware particle cleaning. Acta Crystallographica Sec. D. 82, 9 (2026).

== Abstract ==

Cryo-electron microscopy (cryo-EM) micrographs are frequently contaminated
by carbon edges, ice crystals, ethane bubbles and other high-contrast artifacts.
These contaminants trigger abundant false positives in automated particle
pickers, severely hampering downstream 3D reconstruction. Existing methods
either avoid contamination implicitly (requiring dataset-specific tuning) or rely
on rule-based filters that fail on complex contamination patterns. Here, we
present QwenCryoMarker, a universal post-processing framework that converts
the outputs of arbitrary particle pickers into clean, high-precision particle sets.
Our pipeline consists of two core stages: (i) a visual large model (Qwen-Image-
Edit-2511) fine-tuned via supervised learning to generate pixel-accurate binary
contamination masks from raw micrographs and (ii) a lightweight contaminationaware
filtering module that discards particles when the contamination proportion
within their surrounding circular region exceeds a predefined threshold
ratio. Contamination is defined as all micrograph regions unsuitable for reliable
particle picking and subsequent 3D reconstruction, including carbon edges, ice
crystals, ethane bubbles, miscellaneous debris and dense protein aggregates.
The framework features plug-and-play deployment: it requires no per-dataset
parameter tuning or extra retraining, and maintains compatibility with classical
pickers (blob detection, template matching) as well as deep learning-based
pickers (Topaz, crYOLO etc.). We validate QwenCryoMarker on five diverse
CryoPPP benchmark datasets across four representative particle pickers.
Quantitatively, our method consistently boosts precision with an average
absolute gain of 0.009 and lifts the F1-score, while recall only drops slightly by an
average of 0.008. Segmentation benchmarking shows our model reaches a mean
intersection over union (IoU) of 0.629, surpassing that of the state-of-the-art
MicrographCleaner (0.551) by 14.2%. We further compare against multiple
segmentation baselines: U-Net (0.448), DeepLabV3+ (0.488), SAM (0.475),
ASOCEM (0.195) and IceBreaker (0.433). A downstream reconstruction case
study on EMPIAR-10017 verifies that particles filtered by QwenCryoMarker
yield cleaner 2D class averages and higher resolution 3D density maps (3.88
versus 3.97 A ˚ ). Qualitative visualization also confirms that QwenCryoMarker
stably eliminates false particles located on carbon films and ice crystals, independent
of the upstream particle-picking algorithm. By encapsulating contamination
suppression as a universal, model-agnostic post-processing module,
QwenCryoMarker offers a practical, robust, easy-to-deploy toolkit that greatly
improves particle-set quality without modifying existing cryo-EM workflows.
The framework is fully open-source and can be seamlessly integrated into
mainstream cryo-EM processing pipelines.

== Keywords ==

== Links ==

https://journals.iucr.org/paper?wan5006

== Related software ==

== Related methods ==

== Comments ==

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WikiSysop — Thu, 03 Sep 2026 05:56:45 GMT

Automatic particle picking

← Older revision		Revision as of 05:56, 3 September 2026
Line 1,851:		Line 1,851:
	\| [[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
			\| [[2026Sun_QwenCryoMarker]]
			\| QwenCryoMarker: Identification of contamination in micrographs
	\|-		\|-

2026Kreymer EM

WikiSysop — Mon, 31 Aug 2026 04:37:26 GMT

Created page with "== Citation == Kreymer, S., Singer, A. and Bendory, T. 2026. Expectation-maximization for structure determination directly from cryo-em micrographs. Inverse problems and imaging. 27, (2026), 110. == Abstract == A single-particle cryo-electron microscopy (cryo- EM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional (3-D) molecular structure at unknown locations, taken under unknown viewing directions. A..."

New page

== Citation ==

Kreymer, S., Singer, A. and Bendory, T. 2026. Expectation-maximization for structure determination directly from cryo-em micrographs. Inverse problems and imaging. 27, (2026), 110.

== Abstract ==

A single-particle cryo-electron microscopy (cryo-
EM) measurement, called a micrograph, consists of multiple
two-dimensional tomographic projections of a three-dimensional
(3-D) molecular structure at unknown locations, taken under
unknown viewing directions. All existing cryo-EM algorithmic
pipelines first locate and extract the projection images, and then
reconstruct the structure from the extracted images. However, if
the molecular structure is small, the signal-to-noise ratio (SNR)
of the data is very low, making it challenging to accurately
detect projection images within the micrograph. Consequently,
all standard techniques fail in low-SNR regimes. To recover
molecular structures from measurements of low SNR, and in
particular small molecular structures, we devise an approximate
expectation-maximization algorithm to estimate the 3-D structure
directly from the micrograph, bypassing the need to locate the
projection images. We corroborate our computational scheme
with numerical experiments and present successful structure
recoveries from simulated noisy measurements.

== Keywords ==

== Links ==

https://www.aimsciences.org//article/doi/10.3934/ipi.2026033

== Related software ==

== Related methods ==

== Comments ==

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WikiSysop — Mon, 31 Aug 2026 04:36:40 GMT

3D Reconstruction

← Older revision		Revision as of 04:36, 31 August 2026
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	\| [[2026Chen_MPM]]		\| [[2026Chen_MPM]]
	\| Masked projection modelling for 3D reconstruction		\| Masked projection modelling for 3D reconstruction
			\|-

			\| Paper
			\| [[2026Kreymer_EM]]
			\| Expectation-Maximization reconstruction directly from micrographs
	\|-		\|-