Cells#

Expert cell image processing pipeline.

This module provides the basic routines for processing immediate early gene data.

The routines are used in the ClearMap.Scripts.CellMap pipeline.

detect_cells(source, sink=None, cell_detection_parameter={'background_correction': {'form': 'Disk', 'save': False, 'shape': (10, 10)}, 'dog_filter': {'shape': None, 'sigma': None, 'sigma2': None}, 'equalization': None, 'illumination_correction': {'flatfield': None, 'scaling': 'mean'}, 'intensity_detection': {'measure': ['source', 'background_correction'], 'method': 'max', 'shape': 3}, 'maxima_detection': {'h_max': None, 'save': False, 'shape': 5, 'threshold': 0, 'valid': True}, 'shape_detection': {'save': False, 'threshold': 700}}, processing_parameter={'axes': [2], 'optimization': True, 'optimization_fix': 'all', 'overlap': 32, 'processes': None, 'size_max': 100, 'size_min': 50, 'verbose': None}, workspace=None)[source]#

Cell detection pipeline.

Arguments

sourcesource specification: The source of the stitched raw data.
sinksink specification or None: The sink to write the result to. If None, an array is returned.
cell_detection_parameterdict: Parameter for the binarization. See below for details.
processing_parameterdict: Parameter for the parallel processing. See ClearMap.ParallelProcessing.BlockProcessing.process() for description of all the parameter.
workspace: Workspace: The optional workspace object to have a handle to cancel the multiprocess

Returns

sinkSource: The result of the cell detection.

Notes

Effectively this function performs the following steps:

illumination correction via correct_illumination()
background removal
difference of Gaussians (DoG) filter
maxima detection via find_extended_maxima()
cell shape detection via detect_shape()
cell intensity and size measurements via:
find_intensity(), find_size().

The parameters for each step are passed as sub-dictionaries to the cell_detection_parameter dictionary.

If None is passed for one of the steps this step is skipped.
Each step also has an additional parameter ‘save’ that enables saving
of the result of that step to a file to inspect the pipeline.

Illumination correction

illumination_correctiondict or None

Illumination correction step parameter.

flatfieldarray or str: The flat field estimate for the image planes.
backgroundarray or None: A background level to assume for the flatfield correction.
scalingfloat, ‘max’, ‘mean’ or None: Optional scaling after the flat field correction.
savestr or None: Save the result of this step to the specified file if not None.

Background removal

background_correctiondict or None

Background removal step parameter.

shapetuple: The shape of the structure element to estimate the background. This should be larger than the typical cell size.
formstr: The form of the structure element (e.g. ‘Disk’)
savestr or None: Save the result of this step to the specified file if not None.

Equalization

equalizationdict or None

Equalization step parameter. See also ClearMap.ImageProcessing.LocalStatistics.local_percentile()

percentiletuple: The lower and upper percentiles used to estimate the equalization. The lower percentile is used for normalization, the upper to limit the maximal boost to a maximal intensity above this percentile.
max_valuefloat: The maximal intensity value in the equalized image.
selemtuple: The structural element size to estimate the percentiles. Should be larger than the larger vessels.
spacingtuple: The spacing used to move the structural elements. Larger spacings speed up processing but become locally less precise.
interpolateint: The order of the interpolation used in constructing the full background estimate in case a non-trivial spacing is used.
savestr or None: Save the result of this step to the specified file if not None.

DoG Filter

dog_filterdict or None

Difference of Gaussian filter step parameter.

shapetuple: The shape of the filter. This should be near the typical cell size.
sigmatuple or None: The std of the inner Gaussian. If None, determined automatically from shape.
sigma2tuple or None: The std of the outer Gaussian. If None, determined automatically from shape.
savestr or None: Save the result of this step to the specified file if not None.

Maxima detection

maxima_detectiondict or None

Extended maxima detection step parameter.

h_maxfloat or None: The ‘height’ for the extended maxima. If None, simple local maxima detection is used.
shapetuple: The shape of the structural element for extended maxima detection. This should be near the typical cell size.
thresholdfloat or None: Only maxima above this threshold are detected. If None, all maxima are detected.
validbool: If True, only detect cell centers in the valid range of the blocks with overlap.
savestr or None: Save the result of this step to the specified file if not None.

Shape detection

shape_detectiondict or None

Shape detection step parameter.

thresholdfloat: Cell shape is expanded from maxima if pixels are above this threshold and not closer to another maxima.
savestr or None: Save the result of this step to the specified file if not None.

Intensity detection

intensity_detectiondict or None

Intensity detection step parameter.

method{‘max’|’min’,’mean’|’sum’}: The method to use to measure the intensity of a cell.
shapetuple or None: If no cell shapes are detected a disk of this shape is used to measure the cell intensity.
savestr or None: Save the result of this step to the specified file if not None.

References

[1] Renier, Adams, Kirst, Wu et al., “Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes.”, Cell 165, 1789 (2016) [1] Kirst et al., “Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature”, Cell 180, 780 (2020)

detect_cells_block(source, parameter={'background_correction': {'form': 'Disk', 'save': False, 'shape': (10, 10)}, 'dog_filter': {'shape': None, 'sigma': None, 'sigma2': None}, 'equalization': None, 'illumination_correction': {'flatfield': None, 'scaling': 'mean'}, 'intensity_detection': {'measure': ['source', 'background_correction'], 'method': 'max', 'shape': 3}, 'maxima_detection': {'h_max': None, 'save': False, 'shape': 5, 'threshold': 0, 'valid': True}, 'shape_detection': {'save': False, 'threshold': 700}}, n_threads=None)[source]#: Detect cells in a Block.

detect_maxima(source, h_max=None, shape=5, threshold=None, verbose=False)[source]#

dog_filter(source, shape, sigma=None, sigma2=None)[source]#

equalize(source, percentile=(0.5, 0.95), max_value=1.5, selem=(200, 200, 5), spacing=(50, 50, 5), interpolate=1, mask=None)[source]#

filter_cells(source, sink, thresholds)[source]#

Filter an array of detected cells according to the thresholds.

Arguments

sourcestr, array or Source: The source for the cell data.
sinkstr, array or Source: The sink for the results.
thresholdsdict: Dictionary of the form {name : threshold} where name refers to the column in the cell data and threshold can be None, a float indicating a minimal threshold or a tuple (min, max) where min, max can be None or a minimal and maximal threshold value.

Returns

sinkstr, array or Source: The thresholded cell data.

remove_background(source, shape, form='Disk')[source]#

default_cell_detection_parameter = {'background_correction': {'form': 'Disk', 'save': False, 'shape': (10, 10)}, 'dog_filter': {'shape': None, 'sigma': None, 'sigma2': None}, 'equalization': None, 'illumination_correction': {'flatfield': None, 'scaling': 'mean'}, 'intensity_detection': {'measure': ['source', 'background_correction'], 'method': 'max', 'shape': 3}, 'maxima_detection': {'h_max': None, 'save': False, 'shape': 5, 'threshold': 0, 'valid': True}, 'shape_detection': {'save': False, 'threshold': 700}}#: Parameter for the cell detection pipeline. See detect_cells() for details.

default_cell_detection_processing_parameter = {'axes': [2], 'optimization': True, 'optimization_fix': 'all', 'overlap': 32, 'processes': None, 'size_max': 100, 'size_min': 50, 'verbose': None}#: Parallel processing parameter for the cell detection pipeline. See ClearMap.ParallelProcessing.BlockProcessing.process() for details.