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="Test a range of rescaling factors to get the value with the most accurate cell segmentation. Default is 1.", visibility="MESSAGE") hint2 #@ Float (label="Enter minimum value", value=1.00, min=0.0500, max=10.0) scale_factor_1 #@ Float (label="Enter maximum max value", value=1.50, min=0.0500, max=10.0) scale_factor_2 #@ Float (label="Enter size of each increment step", value=0.2500) step_scale choice_scaling = "Use a scaling factor"; //#@ 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 #@ String(value="If you are getting out of memory errors, you can try increasing the number of tiles./",visibility="MESSAGE", required=false) tile_hint #@ boolean Change_Tile #@ Double (label="No. of tiles", style="spinner", min=1, stepSize=1,value=1) tile_manual cell_type="Cell"; 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; //multiplying by 10 to avoid errors with using float values start = scale_factor_1*10; end = scale_factor_2*10; increment = step_scale*10; print(start,end,increment); //scale_factor_2+=0.00001; //makes sure last for(i=start;i<=end;i+=increment) { //print("Running segmentation on image scaled by: "+scale); //dividing by 10 to get actual scale value scale = i/10; roiManager("reset"); selectWindow(img); //Rescale factor applied to training pixel size of 0.568 //the target image is then rescale to the rescaled pixel size scale_name="Recaling_factor"; target_pixel_size= training_pixel_size/scale; scale_factor = pixelWidth/target_pixel_size; img_seg=scale_name+"_"+scale+"_"+cell_type; //print("Calculated "+scale_name+" of: "+scale_factor); img_seg=scale_name+"_"+scale+"_"+cell_type; print("Running segmentation on image scaled by "+scale_name+" of: "+scale); print("Target pixel size used "+target_pixel_size); 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); if(Change_Tile) tiles = tile_manual; else { 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; } print("No. of tiles "+tiles); //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"); exit("Completed"); //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); }