WikiSysop: Created page with "== Citation == Chung, S.-C. and Chou, P.-C. 2025. CRISP: A modular platform for cryo-EM image segmentation and processing with Conditional Random Field. J. Structural Biology. (2025), 108239. == Abstract == Distinguishing signal from background in cryogenic electron microscopy (cryo-EM) micrographs is a critical processing step but remains challenging owing to the inherently low signal-to-noise ratio (SNR), contaminants, variable ice thickness, and densely packed part..."

2025-09-29T16:31:28Z

Created page with "== Citation == Chung, S.-C. and Chou, P.-C. 2025. CRISP: A modular platform for cryo-EM image segmentation and processing with Conditional Random Field. J. Structural Biology. (2025), 108239. == Abstract == Distinguishing signal from background in cryogenic electron microscopy (cryo-EM) micrographs is a critical processing step but remains challenging owing to the inherently low signal-to-noise ratio (SNR), contaminants, variable ice thickness, and densely packed part..."

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== Citation ==

Chung, S.-C. and Chou, P.-C. 2025. CRISP: A modular platform for cryo-EM image segmentation and processing with Conditional Random Field. J. Structural Biology. (2025), 108239.

== Abstract ==

Distinguishing signal from background in cryogenic electron microscopy (cryo-EM) micrographs is a critical
processing step but remains challenging owing to the inherently low signal-to-noise ratio (SNR), contaminants,
variable ice thickness, and densely packed particles of heterogeneous sizes. Recent image-segmentation
methods provide pixel-level precision and thus offer several advantages over traditional object-detection
approaches: segmented-blob mass can be computed to suppress false-positive particles, particle centering
can be improved by leveraging the full brightness profile, and irregularly shaped particles can be identified
more reliably. However, low SNR makes it difficult to obtain accurate pixel-level annotations for training
segmentation models, and, in the absence of systematic evaluation platforms, most segmentation pipelines
still rely on ad-hoc design choices.
Here, we introduce a modular platform that automatically generates high-quality segmentation maps
to serve as reference labels. The platform supports flexible combinations of segmentation architectures,
feature extractors, and loss functions, and it integrates novel Conditional Random Fields (CRFs) with classdiscriminative
features to refine coarse predictions into fine-grained segmentations. On synthetic data, models
trained with our reference labels achieve pixel-level accuracy, recall, precision, Intersection-over-Union (IoU),
and F1 scores all exceeding 90%. We further show that the resulting segmentations can be used directly
for particle picking, yielding higher-resolution 3D density maps from real experimental datasets; these
reconstructions match those curated by human experts and surpass the results of existing particle-picking
tools. To facilitate further research, we release our methods as the open-source package CRISP, available at
https://github.com/phonchi/CryoParticleSegment.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S1047847725000747

== Related software ==

== Related methods ==

== Comments ==

2025Chung CRISP - Revision history