micrograph-cleaner-em 1.0.2

Deep-learning micrograph denoising/segmentation for cryo-EM (TF2/Keras3 rescue)

MicrographCleaner

MicrographCleaner (micrograph_cleaner_em) is a python package designed to segment cryo-EM micrographs into:

• carbon/high-contrast or contaminated regions
• good regions

so that incorrectly picked coordinates can be easily ruled out

To get a complete description of usage execute

cleanMics -h

Example

cleanMics -c path/to/inputCoords/ -o path/to/outputCoords/ -b 180 -s 1.0 -i /path/to/micrographs/ --predictedMaskDir path/to/store/masks --deepThr 0.5

INSTALLATION:

New! The main branch is quite old and won't work on a modern GPU card. If it is your case, you can try to install from the new tf2 branch

This version has been tested with Python 3.10

1. (optional) create and activate the virtual environment

pip install virtualenv
virtualenv --system-site-packages -p python3 ./env_micrograph_cleaner_em
source ./env_micrograph_cleaner_em/bin/activate

or conda environment

conda create -n env_micrograph_cleaner_em python=3.10
conda activate env_micrograph_cleaner_em

2. Install micrograph_cleaner_em

PyPI

pip install micrograph-cleaner-em

To get the latest version, install from git instead.

git clone https://github.com/rsanchezgarc/micrograph_cleaner_em.git
cd micrograph_cleaner_em
pip install -e .

or

pip install git+https://github.com/rsanchezgarc/micrograph_cleaner_em.git

3. Download deep learning model cleanMics --download

4. Ready

USAGE

MicrographCleaner employs an U-net-based deep learning model to segmentate micrographs into good regions and bad regions. Thus, it is mainly used as a post-processing step after particle picking in which coordinates selected in high contrast artefacts, such as carbon, will be ruled out. Additionally, it can be employed to generate binary masks so that particle pickers can be prevented from considering problematic regions. Thus, micrograph_cleaner employs as a mandatory argument a(some) micrograph(s) fileneame(s) and the particle size in pixels (with respect input mics). Additionally it can recive as input:

1. A directory where picked coordinates are located and another directory where scored/cleaned coordiantes will be saved. Coordinates will be saved in pos format or plain text (columns whith header colnames x and y) are located. There must be one different coordinates file for each micrograph named as the micrograph and the output coordiantes will preserve the naming. E.g. -c path/to/inputCoordsDirectory/ -o /path/to/outputCoordsDirectory/ Allowed formats are xmipp pos, relion star and raw text tab separated with at least two columns named as xcoor, ycoor in the header. Raw text file example:

micFname1.tab:
###########################################
xcoor ycoor otherInfo1 otherInfo2
12  143  -1  0.1
431  4341  0  0.2
323  321  1  0.213
###########################################

2. A directory where predicted masks will be saved (mrc format). E.g. --predictedMaskDir path/where/predictedMasksWillBeSaved/

3. A downsampling factor (can be less than 1 if actually upsampling was performed) in case the coordinates where picked from micrographs at different scale. E.g. -s 2 will downsample coordinates by a factor 2 and then it will apply the predicted mask that is as big as the input micrographs. This case corresponds to an example in which we use for particle picking raw micrographs but we are using MicrographCleaner with downsampled mics

4. Any combination of previous options.

Trained MicrographCleaner model is available here and can be automatically download executing
cleanMics --download

Examples

#Donwload deep learning model
cleanMics --download
    
#Compute masks from imput micrographs and store them
cleanMics -b $BOX_SIXE  -i  /path/to/micrographs/ --predictedMaskDir path/to/store/masks

#Rule out input bad coordinates (threshold<0.5) and store them into path/to/outputCoords
cleanMics  -c path/to/inputCoords/ -o path/to/outputCoords/ -b $BOX_SIXE -s $DOWN_FACTOR  -i  /path/to/micrographs/ --deepThr 0.5

#Compute goodness scores from input coordinates and store them into path/to/outputCoords
cleanMics  -c path/to/inputCoords/ -o path/to/outputCoords/ -b $BOX_SIXE -s $DOWN_FACTOR  -i  /path/to/micrographs/ --deepThr 0.5     

API:

The fundamental class employed within MicrographCleaner is MaskPredictor, a class designed to predict a contamination/carbon mask given a micrograph.

class micrograph_cleaner_em.MaskPredictor

Usage: predicts masks of shape HxW given one numpy array of shape HxW that represents a micrograph. Mask values range from 0. to 1., being 0. associated to clean regions and 1. to contamination.

builder

micrograph_cleaner_em.MaskPredictor(boxSize, deepLearningModelFname=DEFAULT_PATH , gpus=[0], strideFactor=2)
    
    :param boxSize (int): estimated particle boxSize in pixels
    :param deepLearningModelFname (str): a path where the deep learning model will be loaded. DEFAULT_PATH="~/.local/share/micrograph_cleaner_em/models/defaultModel.keras"
    :param gpus (list of gpu ids (ints) or None): If None, CPU only mode will be employed.
    :param strideFactor (int): Overlapping between windows. Micrographs are divided into patches and each processed individually.
                         The overlapping factor indicates how many times a given row/column is processed by the network. The 
                         bigger the better the predictions, but higher computational cost.

methods

predictMask(self, inputMic, preproDownsampleMic=1, outputPrecision=np.float32):
    Obtains a contamination mask for a given inputMic

    :param inputMic (np.array shape HxW): the micrograph to clean
    :param preproDownsampleMic: the downsampling factor applied to the micrograph before processing. Make it bigger if
                   large carbon areas are not identified
    :param outputPrecision: the type of the floating point number desired as input. Default float32
    :return: mask (np.array shape HxW): a mask that ranges from 0. to 1. ->
                   0. meaning clean area and 1. contaminated area.

getDownFactor(self):
    MaskPredictor preprocess micrographs before Nnet computation. First step is donwsampling using a donwsampling factor
    that depends on particle boxSize. This function computes the downsampling factor
    
    :return (float): the donwsampling factor that MaskPredictor uses internally when preprocessing the micrographs
    
close(self):
    Used to release memory

example

The following lines show how to compute the mask for a given micrograph

import numpy as np
import mrcfile
import micrograph_cleaner_em as mce

boxSize = 128 #pixels

# Load the micrograph data, for mrc files you can use mrcifle
# but you can use any other method that return a numpy  array

with mrcfile.open('/path/to/micrograph.mrc') as mrc:
    mic = mrc.data

# By default, the mask predictor will try load the model at  
# "~/.local/share/micrograph_cleaner_em/models/"
# provide , deepLearningModelFname= modelPath argument to the builder 
# if the model is placed in other location 

with mce.MaskPredictor(boxSize, gpus=[0]) as mp:
    mask = mp.predictMask(mic) #by default, mask is float32 numpy array
    
# Then write the mask as a file

with mrcfile.new('mask.mrc', overwrite=True) as maskFile:
    maskFile.set_data(mask.astype(np.half)) # as float

Dataset

The model and dataset used in this work can be downloaded from https://zenodo.org/records/17093439