fidder 0.0.5

U-Net for 2D fiducial segmentation in cryo-EM

fidder

fidder is a Python package for detecting and erasing gold fiducials in cryo-EM images.

Fiducials are detected using a pre-trained residual 2D U-Net pre-trained for semantic segmentation at 8 Å/px. Segmented regions are replaced with white noise matching the local mean and global standard deviation of the image.

The package can be used from both Python and the command line.

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Quickstart

Python

import mrcfile
import torch
from fidder.predict import predict_fiducial_mask
from fidder.erase import erase_masked_region

# load your image
image = torch.tensor(mrcfile.read('my_image_file.mrc'))

# use a pretrained model to predict a mask
mask, probabilities = predict_fiducial_mask(
    image, pixel_spacing=1.35, probability_threshold=0.5
)

# erase fiducials
erased_image = erase_masked_region(image=image, mask=mask)

Command Line

# predict fiducial mask
fidder predict \
--input-image example.mrc \
--probability-threshold 0.5 \
--output-mask mask.mrc

# erase masked region
fidder erase \
--input-image example.mrc \
--input-mask mask.mrc \
--output-image erased.mrc

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Installation

pip:

pip install fidder

Notes

This package provides similar functionality to BoxNet from Warp when retrained for gold fiducial segmentation.

This package was developed to make this functionality available in a standalone, easy to install Python package. The architecture and training data preprocessing are based on the description in the Warp paper.