var fs=File.separator;
setOption("ExpandableArrays", true);
print("\\Clear");
run("Clear Results");
var fiji_dir=getDirectory("imagej");
var gat_dir=fiji_dir+"scripts"+fs+"GAT"+fs+"Tools"+fs+"commands";
gat_settings_path=gat_dir+fs+"gat_settings.ijm";
if(!File.exists(gat_settings_path)) exit("Cannot find settings file. Check: "+gat_settings_path);
run("Results... ", "open="+gat_settings_path);
training_pixel_size=parseFloat(Table.get("Values", 0)); //0.7;
neuron_area_limit=parseFloat(Table.get("Values", 1)); //1500
neuron_seg_lower_limit=parseFloat(Table.get("Values", 2)); //90
neuron_lower_limit=parseFloat(Table.get("Values", 3)); //160
run("Close");
#@ File (style="open", label="Choose the image to segment") path
#@ boolean image_already_open
// String(choices={"Neuron", "Glia"}, style="list") cell_type
#@ File (style="open", label="Choose the StarDist model file based on celltype.",value=fiji_dir) model_file
#@ String(value="Choose either XY pixel size (microns) or scaling factor (scales images by the specified factor)", visibility="MESSAGE") hint
#@ String(choices={"Use pixel size", "Use a scaling factor"}, style="radioButtonHorizontal",label="Choose mode of segmentation") choice_scaling
#@ String(value="Test a range of values for images to figure out the right one that gives accurate cell segmentation.", visibility="MESSAGE") hint2
#@ Double (label="Enter minimum value", value=1, min=0.0500, max=10.000) scale_factor_1
#@ Double (label="Enter maximum max value", value=2.000, min=0.0500, max=0.950) scale_factor_2
#@ Double (label="Enter increment step/s", value=0.2500) step_scale
//#@ boolean Modify_StarDist_Values (description="Tick to modify the values within the StarDist plugin or defaults will be used.")
#@ String(value="Default Probability is 0.5 and Overlap threshold is 0.5. Leave it as default when first trying this.
More info about below parameters can be found here: https://www.imagej.net/StarDist/",visibility="MESSAGE", required=false) hint34
#@ Double (label="Probability (if staining is weak, use low values)", style="slider", min=0, max=1, stepSize=0.05,value=0.55) probability
#@ Double (label="Overlap Threshold", style="slider", min=0, max=1, stepSize=0.05,value=0.5) overlap
cell_type="Cell";
if(choice_scaling=="Use pixel size") Use_pixel_size=true;
else if(choice_scaling=="Use a scaling factor") Use_pixel_size=false;
print("\\Clear");
if(image_already_open==true)
{
waitForUser("Select Image to segment (Image already open was selected)");//. Remember to choose output folder in next prompt");
file_name=getTitle(); //get file name without extension (.lif)
//dir=getDirectory("Choose Output Folder");
}
else
{
if(endsWith(path, ".czi")|| endsWith(path, ".lif")) run("Bio-Formats", "open=["+path+"] color_mode=Composite rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT");
else if (endsWith(path, ".tif")|| endsWith(path, ".tiff")) open(path);
else exit("File type not recognised. Tif, Lif and CZI files supported.");
dir=File.directory;
file_name=File.nameWithoutExtension; //get file name without extension (.lif)
}
//open(path);
run("Select None");
run("Remove Overlay");
print("**Neuron analysis: \nProbability: "+probability+"\nOverlap threshold: "+overlap);
//file_name=File.nameWithoutExtension; //get file name without extension (.lif)
series_stack=getTitle();
//series_stack=getTitle();
dotIndex = indexOf(series_stack, "." );
if(dotIndex>=0) file_name = substring(series_stack,0, dotIndex);
else file_name=series_stack;
Stack.getDimensions(width, height, sizeC, sizeZ, frames);
getPixelSize(unit, pixelWidth, pixelHeight);
neuron_seg_lower_limit=neuron_seg_lower_limit/pixelWidth;
neuron_max_pixels=neuron_area_limit/pixelWidth; //convert micron to pixels
if(unit!="microns" && Use_pixel_size==true) exit("Image not calibrated in microns. Please go to ANalyse->SetScale or Image->Properties to set it for the image");
roiManager("reset");
if(sizeZ>1)
{
print(series_stack+" is a stack");
roiManager("reset");
waitForUser("Note the start and end of the stack.\nPress OK when done");
Dialog.create("Choose slice range");
Dialog.addNumber("Start slice", 1);
Dialog.addNumber("End slice", sizeZ);
Dialog.show();
start=Dialog.getNumber();
end=Dialog.getNumber();
run("Z Project...", "start=&start stop=&end projection=[Max Intensity]");
max_projection=getTitle();
}
else
{
print(series_stack+" has only one slice, assuming its max projection");
max_projection=getTitle();
}
if(sizeC>1)
{
waitForUser("Check image to select the right channel");
channel_seg=getNumber("Enter channel number for "+cell_type, 1);
selectWindow(max_projection);
run("Select None");
run("Remove Overlay");
//run("Duplicate...", "title="+cell_type+" duplicate channels="+channel_seg);
run("Duplicate...", "title="+cell_type+" duplicate channels="+channel_seg);
img=getTitle();
}
else {
selectWindow(max_projection);
run("Select None");
run("Remove Overlay");
run("Duplicate...", "title="+cell_type);
img=getTitle();
}
//replace file separator so stardist can identify right file
model_file=replace(model_file, "\\\\","\\\\\\\\\\\\\\\\");
img_seg_array=newArray();
setOption("ExpandableArrays", true);
idx=0;
for(scale=scale_factor_1;scale<=scale_factor_2;scale+=step_scale)
{
//print("Running segmentation on image scaled by: "+scale);
roiManager("reset");
selectWindow(img);
if(Use_pixel_size == true)
{
//Training images were pixelsize of ~0.378, so scaling images based on this
scale_factor=pixelWidth/scale;
if(scale_factor<1.001 && scale_factor>1) scale_factor=1;
scale_name="Pixel_size";
}
else
{
scale_factor=scale;
scale_name="Scale_factor";
}
img_seg=scale_name+"_"+scale+"_"+cell_type;
print("Running segmentation on image scaled by "+scale_name+" of: "+scale);
if(scale_factor!=1)
{
new_width=round(width*scale_factor);
new_height=round(height*scale_factor);
//print(img_seg);
run("Scale...", "x=- y=- width="+new_width+" height="+new_height+" interpolation=None create title="+img_seg);
}
else
{
selectWindow(img);
run("Select None");
run("Duplicate...", "title="+img_seg);
}
//choice=0;
selectWindow(img_seg);
tiles=4;
if(new_width>2000 || new_height>2000) tiles=8;
if(new_width>5000 || new_height>5000) tiles=12;
else if (new_width>9000 || new_height>5000) tiles=20;
//run segmentation
run("Command From Macro", "command=[de.csbdresden.stardist.StarDist2D],args=['input':'"+img_seg+"', 'modelChoice':'Model (.zip) from File', 'normalizeInput':'true', 'percentileBottom':'1.0', 'percentileTop':'99.8', 'probThresh':'"+probability+"', 'nmsThresh':'"+overlap+"', 'outputType':'Both', 'modelFile':'"+model_file+"', 'nTiles':'"+tiles+"', 'excludeBoundary':'2', 'roiPosition':'Automatic', 'verbose':'false', 'showCsbdeepProgress':'false', 'showProbAndDist':'false'], process=[false]");
wait(50);
//make sure cells are detected.. if not exit macro
if(roiManager("count")==0) exit("No cells detected. Reduce probability or check image.\nAnalysis stopped");
else roiManager("reset");
label_image=getTitle();
selectWindow(label_image);
run("Remove Overlay");
run("Label Size Filtering", "operation=Greater_Than_Or_Equal size="+neuron_seg_lower_limit);
label_filtered=getTitle();
close(label_image);
//run("Remove Border Labels", "left right top bottom");
//wait(10);
//rename("Label-killBorders_"+scale);
run("glasbey_on_dark");
//run("LabelMap to ROI Manager (2D)");
label_to_roi(label_filtered);
wait(20);
selectWindow(img_seg);
run("From ROI Manager");
//close(label_image);
selectWindow(img_seg);
img_seg_array[idx]=img_seg;
idx+=1;
print("No of objects: "+roiManager("count"));
//close("Label-killBorders");
}
run("Cascade");
print("Verify the segmentation in the images: ");
//Array.print(img_seg_array);
function label_to_roi(label_image)
{
roiManager("reset");
// label_image=getTitle();
run("CLIJ2 Macro Extensions", "cl_device=");
Ext.CLIJ2_push(label_image);
//reindex the labels to make labels sequential
Ext.CLIJ2_closeIndexGapsInLabelMap(label_image, reindex);
//statistics of labelled pixels
Ext.CLIJ2_statisticsOfLabelledPixels(reindex, reindex);
Ext.CLIJ2_pull(label_image);
Ext.CLIJ2_pull(reindex);
Ext.CLIJ2_clear();
//get centroid of each label
selectWindow("Results");
x=Table.getColumn("CENTROID_X");
y=Table.getColumn("CENTROID_Y");
//getting the identifiers as the values correspond to the label values
identifier=Table.getColumn("IDENTIFIER");
x1=Table.getColumn("BOUNDING_BOX_X");
y1=Table.getColumn("BOUNDING_BOX_Y");
x2=Table.getColumn("BOUNDING_BOX_END_X");
y2=Table.getColumn("BOUNDING_BOX_END_Y");
//use wand tool to create selection at each label centroid and add the selection to ROI manager
//will not add it if there is no selection or if the background is somehow selected
selectWindow(reindex);
for(i=0;i0 (not background), add ROI
if(selectionType()>0 && intensity>0) { roiManager("add"); }
//if there is no intensity value at the centroid, its probably coz the object is not circular
// and centroid is not in the object
else{
//get the width of the bounding box
x_b=x2[i]-x1[i];
//get the height of the bounding box
y_b=y2[i]-y1[i];
//get y coordinate
//y_temp=y1[i];
//parameters for (Archimedean) spiral
pitch = 4;
angle = 0;
r = 0;
a=0;
//https://forum.image.sc/t/clij-label-map-to-roi-fast-version/51356/11
//spiral search instead brute force search of every pixel
while(r <= x_b/2 || r <= y_b/2)
{
r = sqrt(a)*pitch;
angle += atan(1/r);
x_spiral = (r)*cos(angle*pitch);
y_spiral = (r)*sin(angle*pitch);
intensity=getValue(x[i] + x_spiral, y[i] + y_spiral);
a++;
if(intensity>0 && intensity==identifier[i])
{
doWand(x[i] + x_spiral, y[i] + y_spiral);
roiManager("add");
print(r);
print("AFTER");
r = x_b+1000;
}
}
if(r!=x_b+1000) print("search not successful for "+i);
}
}
close("Results");
close(reindex);
close(label_image);
}