# this is a python rewrite of the original ijm published at
# https://github.com/Hyojung-Choo/Myosoft/blob/Myosoft-hub/Scripts/central%20nuclei%20counter.ijm
# IJ imports
# TODO: are the imports RoiManager and ResultsTable needed when using the services?
from ij import IJ, WindowManager as wm
from ij.plugin import Duplicator, RoiEnlarger, RoiScaler
from trainableSegmentation import WekaSegmentation
from de.biovoxxel.toolbox import Extended_Particle_Analyzer
from ij.measure import ResultsTable
# Bio-formats imports
from loci.plugins import BF
from loci.plugins.in import ImporterOptions
# python imports
import time
import os
#@ String (visibility=MESSAGE, value="<html><b> Welcome to Myosoft - identify fibers! </b></html>") msg1
#@ File (label="Select directory with classifiers", style="directory") classifiers_dir
#@ File (label="Select directory for output", style="directory") output_dir
#@ File (label="Select image file", description="select your image") path_to_image
#@ Boolean (label="close image after processing", description="tick this box when using batch mode", value=False) close_raw
#@ String (visibility=MESSAGE, value="<html><b> Morphometric Gates </b></html>") msg2
#@ Integer (label="Min Area [um²]", value=10) minAr
#@ Integer (label="Max Area [um²]", value=6000) maxAr
#@ Float (label="Min Circularity", value=0.5) minCir
#@ Float (label="Max Circularity", value=1) maxCir
#@ Float (label="Min solidity", value=0.0) minSol
#@ Float (label="Max solidity", value=1) maxSol
#@ Integer (label="Min perimeter [um]", value=5) minPer
#@ Integer (label="Max perimeter [um]", value=300) maxPer
#@ Integer (label="Min min ferret [um]", value=0.1) minMinFer
#@ Integer (label="Max min ferret [um]", value=100) maxMinFer
#@ Integer (label="Min ferret AR", value=0) minFAR
#@ Integer (label="Max ferret AR", value=8) maxFAR
#@ Float (label="Min roundess", value=0.2) minRnd
#@ Float (label="Max roundess", value=1) maxRnd
#@ String (visibility=MESSAGE, value="<html><b> Expand ROIS to match fibers </b></html>") msg3
#@ Float (label="ROI expansion [microns]", value=1) enlarge
#@ String (visibility=MESSAGE, value="<html><b> channel positions in the hyperstack </b></html>") msg5
#@ Integer (label="Membrane staining channel number", style="slider", min=1, max=5, value=1) membrane_channel
#@ Integer (label="Fiber staining (MHC) channel number (0=skip)", style="slider", min=0, max=5, value=3) fiber_channel
#@ Integer (label="minimum fiber intensity (0=auto)", description="0 = automatic threshold detection", value=0) min_fiber_intensity
#@ Integer (label="sub-tiling to economize RAM", style="slider", min=1, max=8, value=4) tiling_factor
#@ RoiManager rm
#@ ResultsTable rt
def fix_ij_options():
"""put IJ into a defined state
"""
# disable inverting LUT
IJ.run("Appearance...", " menu=0 16-bit=Automatic")
# set foreground color to be white, background black
IJ.run("Colors...", "foreground=white background=black selection=red")
# black BG for binary images and pad edges when eroding
IJ.run("Options...", "black pad")
# set saving format to .txt files
IJ.run("Input/Output...", "file=.txt save_column save_row")
# ============= DON’T MOVE UPWARDS =============
# set "Black Background" in "Binary Options"
IJ.run("Options...", "black")
# scale when converting = checked
IJ.run("Conversions...", "scale")
def fix_ij_dirs(path):
"""use forward slashes in directory paths
Parameters
----------
path : string
a directory path obtained from dialogue or script parameter
Returns
-------
string
a more robust path with forward slashes as separators
"""
fixed_path = str(path).replace("\\", "/")
fixed_path = fixed_path + "/"
return fixed_path
def open_image_with_BF(path_to_file):
""" use Bio-Formats to opens the first image from an image file path
Parameters
----------
path_to_file : string
path to the image file
Returns
-------
ImagePlus
the first imp stored in a give file
"""
options = ImporterOptions()
options.setColorMode(ImporterOptions.COLOR_MODE_GRAYSCALE)
options.setAutoscale(True)
options.setId(path_to_file)
imps = BF.openImagePlus(options) # is an array of ImagePlus
return imps[0]
def fix_BF_czi_imagetitle(imp):
image_title = os.path.basename( imp.getTitle() )
image_title = image_title.replace(".czi", "")
image_title = image_title.replace(" ", "_")
image_title = image_title.replace("_-_", "")
image_title = image_title.replace("__", "_")
image_title = image_title.replace("#", "Series")
return image_title
def preprocess_membrane_channel(imp):
"""apply myosoft pre-processing steps for the membrane channel
Parameters
----------
imp : ImagePlus
a single channel image of the membrane staining
"""
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
IJ.run(imp, "Apply LUT", "")
IJ.run(imp, "Enhance Contrast", "saturated=1")
IJ.run(imp, "8-bit", "")
IJ.run(imp, "Invert", "")
IJ.run(imp, "Convolve...", "text1=[-1.0 -1.0 -1.0 -1.0 -1.0\n-1.0 -1.0 -1.0 -1.0 0\n-1.0 -1.0 24.0 -1.0 -1.0\n-1.0 -1.0 -1.0 -1.0 -1.0\n-1.0 -1.0 -1.0 -1.0 0] normalize")
def get_threshold_from_method(imp, channel, method):
"""returns the threshold value of chosen IJ AutoThreshold method in desired channel
Parameters
----------
imp : ImagePlus
the imp from which to get the threshold value
channel : integer
the channel in which to get the treshold
method : string
the AutoThreshold method to use
Returns
-------
list
the upper and the lower threshold (integer values)
"""
imp.setC(channel) # starts at 1
ip = imp.getProcessor()
ip.setAutoThreshold(method + " dark")
lower_thr = ip.getMinThreshold()
upper_thr = ip.getMaxThreshold()
ip.resetThreshold()
return lower_thr, upper_thr
def apply_weka_model(model_path, imp, tiles_per_dim):
"""apply a pretrained WEKA model to an ImagePlus
Parameters
----------
model_path : string
path to the model file
imp : ImagePlus
ImagePlus to apply the model to
tiles_per_dim : integer
tiles the imp to save RAM
Returns
-------
ImagePlus
the result of the WEKA segmentation. One channel per class.
"""
segmentator = WekaSegmentation()
segmentator.loadClassifier( model_path )
result = segmentator.applyClassifier( imp, [tiles_per_dim, tiles_per_dim], 0, True ) #ImagePlus imp, int[x,y,z] tilesPerDim, int numThreads (0=all), boolean probabilityMaps
return result
def process_weka_result(imp):
"""apply myosoft pre-processing steps for the imp after WEKA classification to prepare it
for ROI detection with the extended particle analyzer
Parameters
----------
imp : ImagePlus
a single channel (= desired class) of the WEKA classification result imp
"""
IJ.run(imp, "8-bit", "")
IJ.run(imp, "Median...", "radius=3")
IJ.run(imp, "Gaussian Blur...", "sigma=2")
IJ.run(imp, "Auto Threshold", "method=MaxEntropy")
IJ.run(imp, "Invert", "")
def delete_channel(imp, channel_number):
"""delete a channel from target imp
Parameters
----------
imp : ImagePlus
the imp from which to delete target channel
channel_number : integer
the channel number to be deleted. starts at 0.
"""
imp.setC(channel_number)
IJ.run(imp, "Delete Slice", "delete=channel")
def run_extended_particle_analyzer( imp, eda_parameters ):
"""identifies ROIs in target imp using the extended particle analyzer of the BioVoxxel toolbox
with given parameters
Parameters
----------
imp : ImagePlus
the image on which to run the EPA on. Should be 8-bit thresholded
eda_parameters : array
all user defined parameters to restrict ROI identification
"""
epa = Extended_Particle_Analyzer()
epa.readInputImageParameters(imp)
epa.setDefaultParameterFields()
# expose all parameters explicitly
epa.usePixel = False
epa.usePixelForOutput = False
epa.Area = str(eda_parameters[0]) + "-" + str(eda_parameters[1])
epa.Extent = "0.00-1.00"
epa.Perimeter = str(eda_parameters[2]) + "-" + str(eda_parameters[3])
epa.Circularity = str(eda_parameters[4]) + "-" + str(eda_parameters[5])
epa.Roundness = str(eda_parameters[6]) + "-" + str(eda_parameters[7])
epa.Solidity = str(eda_parameters[8]) + "-" + str(eda_parameters[9])
epa.Compactness = "0.00-1.00"
epa.AR = "0-Infinity"
epa.FeretAR = str(eda_parameters[10]) + "-" + str(eda_parameters[11])
epa.EllipsoidAngle = "0-180"
epa.MaxFeret = "0-Infinity"
epa.MinFeret = str(eda_parameters[12]) + "-" + str(eda_parameters[13])
epa.FeretAngle = "0-180"
epa.COV = "0.00-1.00"
epa.Output = "Nothing"
epa.Redirect = "None"
epa.Correction = "None"
epa.Reset = False
epa.DisplayResults = False
epa.ClearResults = False
epa.Summarize = False
epa.AddToManager = True
epa.ExcludeEdges = False
epa.IncludeHoles = False
epa.defineParticleAnalyzers()
epa.particleAnalysis( imp.getProcessor(), imp, imp.getTitle() )
def measure_in_all_rois( imp, channel, rm ):
"""measures in all ROIS on a given channel of imp all parameters that are set in IJ "Set Measurements"
Parameters
----------
imp : ImagePlus
the imp to measure on
channel : integer
the channel to measure in. starts at 1.
rm : RoiManager
a reference of the IJ-RoiManager
"""
imp.setC(channel)
rm.runCommand(imp,"Deselect")
rm.runCommand(imp,"Measure")
def change_all_roi_color( rm, color ):
"""change the color of all ROIs in the RoiManager
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
color : string
the desired color. e.g. "green", "red", "yellow", "magenta" ...
"""
number_of_rois = rm.getCount()
for roi in range( number_of_rois ):
rm.select(roi)
rm.runCommand("Set Color", color)
def change_subset_roi_color( rm, selected_rois, color ):
"""change the color of selected ROIs in the RoiManager
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
selected_rois : array
ROIs in the RoiManager to change
color : string
the desired color. e.g. "green", "red", "yellow", "magenta" ...
"""
rm.runCommand("Deselect")
rm.setSelectedIndexes(selected_rois)
rm.runCommand("Set Color", color)
rm.runCommand("Deselect")
def show_all_rois_on_image(rm, imp):
"""shows all ROIs in the ROiManager on imp
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
imp : ImagePlus
the imp on which to show the ROIs
"""
imp.show()
rm.runCommand(imp,"Show All")
def save_all_rois(rm, target):
"""save all ROIs in the RoiManager as zip to target path
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
target : string
the path in to store the ROIs. e.g. /my-images/resulting_rois.zip
"""
rm.runCommand("Save", target)
def save_selected_rois( rm, selected_rois, target ):
"""save selected ROIs in the RoiManager as zip to target path
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
selected_rois : array
ROIs in the RoiManager to save
target : string
the path in to store the ROIs. e.g. /my-images/resulting_rois_subset.zip
"""
rm.runCommand("Deselect")
rm.setSelectedIndexes(selected_rois)
rm.runCommand("save selected", target)
rm.runCommand("Deselect")
def enlarge_all_rois( amount_in_um, rm, pixel_size_in_um ):
"""enlarges all ROIs in the RoiManager by x scaled units
Parameters
----------
amount_in_um : float
the value by which to enlarge in scaled units, e.g 3.5
rm : RoiManager
a reference of the IJ-RoiManager
pixel_size_in_um : float
the pixel size, e.g. 0.65 px/um
"""
amount_px = amount_in_um / pixel_size_in_um
all_rois = rm.getRoisAsArray()
rm.reset()
for roi in all_rois:
enlarged_roi = RoiEnlarger.enlarge(roi, amount_px)
rm.addRoi(enlarged_roi)
def select_positive_fibers( imp, channel, rm, min_intensity ):
"""For all ROIs in the RoiManager, select ROIs based on intensity measurement in given channel of imp.
See https://imagej.nih.gov/ij/developer/api/ij/process/ImageStatistics.html
Parameters
----------
imp : ImagePlus
the imp on which to measure
channel : integer
the channel on which to measure. starts at 1
rm : RoiManager
a reference of the IJ-RoiManager
min_intensity : integer
the selection criterion (here: intensity threshold)
Returns
-------
array
a selection of ROIs which passed the selection criterion (are above the threshold)
"""
imp.setC(channel)
all_rois = rm.getRoisAsArray()
selected_rois = []
for i, roi in enumerate(all_rois):
imp.setRoi(roi)
stats = imp.getStatistics()
if stats.mean > min_intensity:
selected_rois.append(i)
return selected_rois
def preset_results_column( rt, column, value):
"""pre-set all rows in given column of the IJ-ResultsTable with desired value
Parameters
----------
rt : ResultsTable
a reference of the IJ-ResultsTable
column : string
the desired column. will be created if it does not yet exist
value : string or float or integer
the value to be set
"""
for i in range( rt.size() ):
rt.setValue(column, i, value)
rt.show("Results")
def add_results( rt, column, row, value ):
"""adds a value in desired rows of a given column
Parameters
----------
rt : ResultsTable
a reference of the IJ-ResultsTable
column : string
the column in which to add the values
row : array
the row numbers in which too add the values.
value : string or float or integer
the value to be set
"""
for i in range( len( row ) ):
rt.setValue(column, row[i], value)
rt.show("Results")
def enhance_contrast( imp ):
"""use "Auto" Contrast & Brightness settings in each channel of imp
Parameters
----------
imp : ImagePlus
the imp on which to change C&B
"""
for channel in range( imp.getDimensions()[2] ):
imp.setC(channel + 1) # IJ channels start at 1
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
def renumber_rois(rm):
"""rename all ROIs in the RoiManager according to their number
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
"""
number_of_rois = rm.getCount()
for roi in range( number_of_rois ):
rm.rename( roi, str(roi + 1) )
def setup_defined_ij(rm, rt):
"""set up a clean and defined Fiji user environment
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
rt : ResultsTable
a reference of the IJ-ResultsTable
"""
fix_ij_options()
rm.runCommand('reset')
rt.reset()
IJ.log("\\Clear")
execution_start_time = time.time()
setup_defined_ij(rm, rt)
print rt.size()
# open image using Bio-Formats
path_to_image = fix_ij_dirs(path_to_image)
raw = open_image_with_BF(path_to_image)
# get image info
raw_image_calibration = raw.getCalibration()
raw_image_title = fix_BF_czi_imagetitle(raw)
# take care of paths and directories
output_dir = fix_ij_dirs(output_dir) + str(raw_image_title) + "/1_identify_fibers/"
if not os.path.exists( output_dir ):
os.makedirs( output_dir )
classifiers_dir = fix_ij_dirs(classifiers_dir)
primary_model = classifiers_dir + "primary.model"
secondary_model = classifiers_dir + "secondary_central_nuclei.model"
# update the log for the user
IJ.log( "Now working on " + str(raw_image_title) )
if raw_image_calibration.scaled() == False:
IJ.log("Your image is not spatially calibrated! Size measurements are only possible in [px].")
IJ.log( " -- settings used -- ")
IJ.log( "area = " + str(minAr) + "-" + str(maxAr) )
IJ.log( "perimeter = " + str(minPer) + "-" + str(maxPer) )
IJ.log( "circularity = " + str(minCir) + "-" + str(maxCir) )
IJ.log( "roundness = " + str(minRnd) + "-" + str(maxRnd) )
IJ.log( "solidity = " + str(minSol) + "-" + str(maxSol) )
IJ.log( "feret_ar = " + str(minFAR) + "-" + str(maxFAR) )
IJ.log( "min_feret = " + str(minMinFer) + "-" + str(maxMinFer) )
IJ.log( "ROI expansion [microns] = " + str(enlarge) )
IJ.log( "Membrane channel = " + str(membrane_channel) )
IJ.log( "MHC positive fiber channel = " + str(fiber_channel) )
IJ.log( "sub-tiling = " + str(tiling_factor) )
IJ.log( " -- settings used -- ")
# image (pre)processing and segmentation (-> ROIs)
membrane = Duplicator().run(raw, membrane_channel, membrane_channel, 1, 1, 1, 1) # imp, firstC, lastC, firstZ, lastZ, firstT, lastT
preprocess_membrane_channel(membrane)
weka_result1 = apply_weka_model(primary_model, membrane, tiling_factor )
delete_channel(weka_result1, 1)
weka_result2 = apply_weka_model(secondary_model, weka_result1, tiling_factor )
delete_channel(weka_result2, 1)
weka_result2.setCalibration(raw_image_calibration)
process_weka_result(weka_result2)
IJ.saveAs(weka_result2, "Tiff", output_dir + raw_image_title + "_all_fibers_binary")
eda_parameters = [minAr, maxAr, minPer, maxPer, minCir, maxCir, minRnd, maxRnd, minSol, maxSol, minFAR, maxFAR, minMinFer, maxMinFer]
raw.show() # EPA will not work if no image is shown
run_extended_particle_analyzer(weka_result2, eda_parameters)
# modify rois
rm.hide()
raw.hide()
enlarge_all_rois( enlarge, rm, raw_image_calibration.pixelWidth )
renumber_rois(rm)
save_all_rois( rm, output_dir + "all_fiber_rois.zip" )
# check for positive fibers
if fiber_channel > 0:
if min_fiber_intensity == 0:
min_fiber_intensity = get_threshold_from_method(raw, fiber_channel, "Mean")[0]
IJ.log( "automatic intensity threshold detection: True" )
IJ.log( "fiber intensity threshold: " + str(min_fiber_intensity) )
change_all_roi_color(rm, "blue")
positive_fibers = select_positive_fibers( raw, fiber_channel, rm, min_fiber_intensity )
change_subset_roi_color(rm, positive_fibers, "magenta")
save_selected_rois( rm, positive_fibers, output_dir + "mhc_positive_fiber_rois.zip")
# measure size & shape, save
IJ.run("Set Measurements...", "area perimeter shape feret's redirect=None decimal=4")
IJ.run("Clear Results", "")
measure_in_all_rois( raw, membrane_channel, rm )
rt = ResultsTable.getResultsTable("Results")
print rt.size()
if fiber_channel > 0:
print rt.size()
preset_results_column( rt, "MHC Positive Fibers (magenta)", "NO" )
print rt.size()
add_results( rt, "MHC Positive Fibers (magenta)", positive_fibers, "YES")
print rt.size()
rt.save(output_dir + "all_fibers_results.csv")
print "saved the all_fibers_results.csv"
# dress up the original image, save a overlay-png, present original to the user
rm.show()
raw.show()
show_all_rois_on_image( rm, raw )
raw.setDisplayMode(IJ.COMPOSITE)
enhance_contrast( raw )
IJ.run("From ROI Manager", "") # ROIs -> overlays so they show up in the saved png
qc_duplicate = raw.duplicate()
IJ.saveAs(qc_duplicate, "PNG", output_dir + raw_image_title + "_all_fibers")
qc_duplicate.close()
wm.toFront( raw.getWindow() )
IJ.run("Remove Overlay", "")
raw.setDisplayMode(IJ.GRAYSCALE)
show_all_rois_on_image( rm, raw )
total_execution_time_min = (time.time() - execution_start_time) / 60.0
IJ.log("total time in minutes: " + str(total_execution_time_min))
IJ.log( "~~ all done ~~" )
IJ.selectWindow("Log")
IJ.saveAs("Text", str(output_dir + raw_image_title + "_all_fibers_Log"))
if close_raw == True:
raw.close()