xmipp3.protocols.protocol_extract_particles module
- class xmipp3.protocols.protocol_extract_particles.XmippProtExtractParticles(**kwargs)[source]
Bases:
ProtExtractParticles,XmippProtocolExtracts particle images from micrographs based on provided coordinates. This essential step prepares particle stacks for further processing such as classification and reconstruction.
AI Generated:
## Overview
The Extract Particles protocol cuts out particle images from micrographs using a set of input coordinates. This is one of the central steps in a cryo-EM workflow, because it transforms the coordinate list produced by picking into an actual stack of particle images that can later be classified, aligned, and reconstructed.
In practical use, extraction is much more than simple cropping. The protocol can also rescale the particles, remove extreme pixel outliers, invert contrast, apply phase flipping, normalize the images, and annotate each particle with local quality descriptors. These operations make the extracted particles more suitable for downstream analysis and can strongly influence the quality of 2D classes and later 3D reconstructions.
For a biological user, the main idea is that this protocol defines exactly what the “working particle images” will look like in the rest of the project. Choices made here affect both interpretability and the behavior of later algorithms.
## Inputs and General Workflow
The protocol starts from a set of coordinates, and from the micrographs associated with those coordinates. In some workflows, the extraction can also be performed from a different set of micrographs, provided that the relationship between the coordinates and the micrographs is still valid.
For each micrograph, the protocol reads the coordinate positions, optionally rescales them if the extraction micrographs differ from the picking micrographs, and cuts particle boxes centered at those positions. Before or during extraction, the protocol may apply several preprocessing operations to the micrograph or to the particle images, such as dust removal, downsampling, phase flipping, contrast inversion, and normalization.
The final output is a set of particles ready for downstream processing.
## Choosing the Particle Box Size
The particle box size is one of the most important parameters. It defines how large the extracted particle image will be, in pixels.
Biologically and practically, the box should be large enough to contain the full particle plus a surrounding region of background. The background is important because many downstream methods use it to estimate noise statistics and to stabilize alignment. If the box is too small, part of the particle may be cut off, which can strongly damage classification and refinement. If it is too large, unnecessary background is added, increasing computational cost and sometimes making alignment harder.
The protocol suggests a box size close to 1.5 times the picking box size, which is often a good starting point. In routine workflows, users should visually inspect a few extracted particles to verify that the particle fits comfortably within the box without wasting too much empty area.
## Rescaling and Downsampling
The protocol can optionally rescale particles during extraction. This can be done by specifying a downsampling factor, a target output box size, or a target sampling rate.
This option is especially useful in early stages of processing. Larger particles at very fine sampling may be unnecessarily expensive for initial 2D classification, whereas a moderate downsampling often speeds up processing considerably without compromising early decisions.
When particles are rescaled, the protocol also adjusts the coordinate scaling and the extraction box size accordingly. An advanced option allows the user to force the output particle box size, which can be useful in specialized workflows but should be used carefully because it may break the original size ratio between picking and extraction.
From a biological point of view, rescaling does not change the underlying specimen, but it changes how finely the image is sampled. A strong downsampling can remove high-resolution information, so it is usually appropriate for exploratory or initial classification steps rather than for the final high-resolution stages.
## Border Handling
By default, Xmipp skips particles whose boxes would extend outside the micrograph borders. This is often a sensible behavior because border particles are incomplete and may be problematic in downstream analysis.
However, the protocol allows the user to fill pixels outside the borders with the nearest available pixel values. This makes it possible to retain particles near the edges of the micrograph.
In practice, this option is only useful if those edge particles are expected to be important. In most workflows, excluding border particles is safer, since partially boxed particles often behave poorly in classification.
## Dust Removal
The protocol can perform dust removal, which replaces extreme pixel outliers with values drawn from a more normal image background distribution.
This step is recommended in many cryo-EM workflows because micrographs may contain very bright or very dark artifacts caused by detector defects, contaminants, or occasional image anomalies. Such pixels can interfere with normalization and classification.
The dust threshold controls how extreme a pixel must be before it is replaced. For cryo-EM data, moderate thresholds are usually appropriate. For negative stain or other higher-contrast images, users may need to be more conservative, since the true signal itself may become extreme.
Biologically, dust removal is not meant to alter the particle signal. Its purpose is to eliminate obvious non-biological artifacts.
## Contrast Inversion
The protocol can invert image contrast during extraction. This is often necessary because different software packages expect particles with opposite contrast conventions.
In most standard cryo-EM workflows using Xmipp, Relion, Spider, or EMAN conventions, particles are usually expected to be white on a dark background. If the raw micrographs show particles as dark features on a bright background, inversion is recommended.
This step does not change the structure, only the sign convention of the image. Nevertheless, it is important because using the wrong contrast convention can confuse downstream methods or make visual inspection misleading.
## Phase Flipping
The protocol can apply CTF phase flipping during extraction, provided that CTF information is available for the micrographs.
Phase flipping compensates for the phase reversals introduced by the contrast transfer function. This is a relatively simple form of CTF correction and is useful in some processing traditions, particularly in Xmipp and EMAN workflows.
However, not all packages expect or require phase-flipped particles. Some modern refinement programs prefer to work with unflipped particles and model the CTF internally. Therefore, this option should be chosen according to the overall workflow.
From a biological perspective, phase flipping can improve the coherence of particle images, but only if the CTF estimation is reliable. It should not be used unless valid CTF information is attached to the micrographs.
## Normalization
The protocol can normalize the extracted particles, which is strongly recommended in most workflows. Normalization places the particle images on a more consistent intensity scale and typically forces the background to have approximately zero mean and unit variance.
Several normalization strategies are available. The simplest normalizes the whole image. More refined methods estimate the background from pixels outside a given circular radius and normalize with respect to that background. The Ramp option also subtracts a background trend before normalization.
The background radius determines which pixels are considered background. If set automatically, the protocol uses approximately half the box size. This works well in many cases, but users should ensure that the chosen background region is mostly outside the particle.
Normalization is extremely important biologically and computationally because it makes particles more comparable across micrographs and imaging conditions.
## Patch Size and Local Micrograph Quality Measures
An additional feature of this protocol is that it computes local descriptors of the micrograph region around each coordinate, including measures related to local variance and Gini coefficient. These can help characterize whether a particle lies in a noisy or heterogeneous local region of the micrograph.
The patch size controls the neighborhood used for this local analysis. If not set manually, the protocol uses a value related to the particle box size.
These descriptors do not directly alter the particles, but they are useful for later quality analysis or filtering. In practice, they provide extra information about whether a coordinate came from a locally stable or problematic region of the micrograph.
## Coordinate Scaling and Different Micrograph Sources
If extraction is performed from a micrograph set different from the one used for picking, the protocol automatically handles the difference in sampling rate and rescales the coordinates accordingly.
This is important in workflows where picking was done on one representation of the micrographs and extraction is performed on another. The protocol is designed to preserve geometric consistency, but users should still make sure that the coordinate-to-micrograph relationship is correct.
From a practical point of view, this feature is very useful in multiscale workflows, where picking may happen on downsampled micrographs and extraction on full-resolution ones.
## Outputs and Their Interpretation
The protocol produces a set of extracted particles. Each particle retains its coordinate and micrograph association, and when available it may also carry CTF information and local quality descriptors.
These particles are the main working dataset for downstream cryo-EM analysis. Their appearance, sampling, normalization state, and possible phase flipping all depend on the choices made in this protocol.
Biologically, the output should be understood as the standardized image representation of the experimental particle projections that will be used for all later computational interpretation.
## Practical Recommendations
A good general strategy is to begin with a reasonable box size that comfortably contains the particle plus background, and to keep normalization enabled. Dust removal is also usually beneficial.
If the dataset is large or the particle is big, moderate rescaling can greatly accelerate initial classification. For final high-resolution work, however, it is often preferable to return to particles extracted at the most appropriate sampling.
Contrast inversion should be chosen according to the requirements of the downstream software. Phase flipping should only be used when it is consistent with the rest of the workflow and when reliable CTF estimates are available.
Users should also inspect a representative subset of extracted particles visually. This remains one of the best ways to verify that the box size, normalization, and contrast choices are sensible.
## Final Perspective
The Extract Particles protocol is one of the key points where raw cryo-EM data become a workable particle dataset. It does not just crop boxes: it defines the image representation that downstream algorithms will receive.
For most cryo-EM users, careful configuration of this protocol is essential. Good extraction choices can make the rest of the workflow easier, cleaner, and more robust, while poor choices can propagate problems throughout classification and reconstruction.
- RESIZE_DIMENSIONS = 1
- RESIZE_FACTOR = 0
- RESIZE_SAMPLINGRATE = 2
- getBoxScale()[source]
Computing the sampling factor between input and output. We should take into account the differences in sampling rate between micrographs used for picking and the ones used for extraction. The downsampling factor could also affect the resulting scale.