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_Analyse Neuron (Multi-channel).ijm
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_Analyse Neuron (Multi-channel).ijm
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//*******
// Author: Pradeep Rajasekhar
// March 2023
// License: BSD3
//
// Copyright 2023 Pradeep Rajasekhar, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
//
// Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
// 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
// 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
//FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
//BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
var fs=File.separator;
setOption("ExpandableArrays", true);
print("\\Clear");
run("Clear Results");
roiManager("reset");
//get fiji directory and get the macro folder for GAT
var fiji_dir=getDirectory("imagej");
var gat_dir=fiji_dir+"scripts"+fs+"GAT"+fs+"Tools"+fs+"commands";
//specify directory where StarDist models are stored
var models_dir=fiji_dir+"models"+fs; //"scripts"+fs+"GAT"+fs+"Models"+fs;
//settings for GAT
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
backgrnd_radius=parseFloat(Table.get("Values", 4));
probability=parseFloat(Table.get("Values", 5)); //prob neuron
probability_subtype=parseFloat(Table.get("Values", 6)); //prob subtype
overlap= parseFloat(Table.get("Values", 7));
overlap_subtype=parseFloat(Table.get("Values", 8));
//get paths of model files
neuron_model_file = Table.getString("Values", 9);
neron_subtype_file = Table.getString("Values", 10);
selectWindow("Results");
run("Close");
//Neuron segmentation model
neuron_model_path=models_dir+neuron_model_file;
//Marker segmentation model
subtype_model_path=models_dir+neron_subtype_file;
if(!File.exists(neuron_model_path)||!File.exists(subtype_model_path)) exit("Cannot find models for segmenting neurons at these paths:\n"+neuron_model_path+"\n"+subtype_model_path);
//check if required plugins are installed
var check_plugin=gat_dir+fs+"check_plugin.ijm";
if(!File.exists(check_plugin)) exit("Cannot find check plugin macro. Returning: "+check_plugin);
runMacro(check_plugin);
//check if label to roi macro is present
var label_to_roi=gat_dir+fs+"Convert_Label_to_ROIs.ijm";
if(!File.exists(label_to_roi)) exit("Cannot find label to roi script. Returning: "+label_to_roi);
//check if roi to label macro is present
var roi_to_label=gat_dir+fs+"Convert_ROI_to_Labels.ijm";
if(!File.exists(roi_to_label)) exit("Cannot find roi to label script. Returning: "+roi_to_label);
//check if ganglia cell count is present
var ganglia_cell_count=gat_dir+fs+"Calculate_Neurons_per_Ganglia.ijm";
if(!File.exists(ganglia_cell_count)) exit("Cannot find ganglia cell count script. Returning: "+ganglia_cell_count);
//check if ganglia cell count is present
var ganglia_label_cell_count=gat_dir+fs+"Calculate_Neurons_per_Ganglia_label.ijm";
if(!File.exists(ganglia_label_cell_count)) exit("Cannot find ganglia label image cell count script. Returning: "+ganglia_label_cell_count)
//check if ganglia prediction macro present
var segment_ganglia=gat_dir+fs+"Segment_Ganglia.ijm";
if(!File.exists(segment_ganglia)) exit("Cannot find segment ganglia script. Returning: "+segment_ganglia);
//check if ganglia hu expansion macro present
var ganglia_hu_expansion=gat_dir+fs+"ganglia_hu.ijm";
if(!File.exists(ganglia_hu_expansion)) exit("Cannot find hu expansion script. Returning: "+ganglia_hu_expansion);
//check if spatial analysis scripts are present
var spatial_single_cell_type=gat_dir+fs+"spatial_single_celltype.ijm";
if(!File.exists(spatial_single_cell_type)) exit("Cannot find single cell spatial analysis script. Returning: "+spatial_single_cell_type);
var spatial_two_cell_type=gat_dir+fs+"spatial_two_celltype.ijm";
if(!File.exists(spatial_two_cell_type)) exit("Cannot find spatial analysis script. Returning: "+spatial_two_cell_type);
var spatial_hu_marker_cell_type=gat_dir+fs+"spatial_hu_marker.ijm";
if(!File.exists(spatial_hu_marker_cell_type)) exit("Cannot find hu_marker spatial analysis script. Returning: "+spatial_hu_marker_cell_type);
//check if import custom ganglia rois script is present
var ganglia_custom_roi=gat_dir+fs+"ganglia_custom_roi.ijm";
if(!File.exists(ganglia_custom_roi)) exit("Cannot find single ganglia custom roi script. Returning: "+ganglia_custom_roi);
//check if import save centroids script is present
var save_centroids=gat_dir+fs+"save_centroids.ijm";
if(!File.exists(save_centroids)) exit("Cannot find save_centroids custom roi script. Returning: "+save_centroids);
//check if import ganglia fix missing neurons script is present
var ganglia_fix_missing_neurons=gat_dir+fs+"ganglia_fix_missing_neurons.ijm";
if(!File.exists(ganglia_fix_missing_neurons)) exit("Cannot find ganglia_fix_missing_neurons custom roi script. Returning: "+ganglia_fix_missing_neurons);
//check if rename_rois script is present
var rename_rois=gat_dir+fs+"rename_rois.ijm";
if(!File.exists(rename_rois)) exit("Cannot find rename_rois custom roi script. Returning: "+rename_rois);
//check if save_roi_composite_img is present
var save_composite_img=gat_dir+fs+"save_roi_composite_img.ijm";
if(!File.exists(save_composite_img)) exit("Cannot find save_composite_img custom roi script. Returning: "+save_composite_img);
fs = File.separator; //get the file separator for the computer (depending on operating system)
#@ File (style="open", label="<html>Choose the image to segment.<br><b>Enter NA if field is empty.</b><html>", value=fiji_dir) path
#@ boolean image_already_open
#@ String(value="<html>If image is already open, tick above box.<html>", visibility="MESSAGE") hint1
// File (style="open", label="<html>Choose the StarDist model file if segmenting neurons.<br>Enter NA if empty<html>",value="NA", description="Enter NA if nothing") neuron_model_path
#@ String(label="Enter channel number for Hu if you know. Enter NA if not using.", value="NA") cell_channel
#@ String(value="<html>----------------------------------------------------------------------------------------------------------------------------------------<html>",visibility="MESSAGE") divider
#@ String(value="<html><center><b>NEURONAL SUBTYPE ANALYSIS</b></center> <html>",visibility="MESSAGE") hint_subtype
//#@ String(value="<html>Tick box below if you want to estimate proportion of neuronal subtypes.<html>", visibility="MESSAGE") hint3
//#@ boolean Calculate_Neuron_Subtype
// File (style="open", label="<html>Choose the StarDist model for subtype segmentation.<br>Enter NA if empty<html>",value="NA", description="Enter NA if nothing") subtype_model_path
cell_type="Hu";
#@ String(value="<html> Channel details should be entered if calculating neuron subtype<br/> The order of channel numbers MUST match with channel name order.<html>",visibility="MESSAGE") hint5
//Enter_channel_details_now=true;
#@ boolean Enter_channel_details_now
#@ String(label="Enter channel names followed by a comma (,). Enter NA if not using.", value="NA") marker_names_manual
#@ String(label="Enter channel numbers with separated by a comma (,). Leave as NA if not using.", value="NA") marker_no_manual
#@ String(value="<html>----------------------------------------------------------------------------------------------------------------------------------------<html>",visibility="MESSAGE") divider
#@ String(value="<html><center><b>DETERMINE GANGLIA OUTLINE</b></center> <html>",visibility="MESSAGE") hint_ganglia
#@ String(value="<html> Cell counts per ganglia will be calculated<br/>Requires a neuron channel & second channel that labels the neuronal fibres.<html>",visibility="MESSAGE") hint4
#@ boolean Cell_counts_per_ganglia (description="Use a pretrained model, Hu or manually define ganglia outline")
#@ String(choices={"DeepImageJ","Define ganglia using Hu","Manually draw ganglia","Import custom ROI"}, style="radioButtonHorizontal") Ganglia_detection
#@ String(label="<html> Enter the channel number for segmenting ganglia.<br/> Not valid for 'Define ganglia using Hu and Import custom ROI'.<br/> Enter NA if not using.<html> ", value="NA") ganglia_channel
#@ String(value="<html>----------------------------------------------------------------------------------------------------<html>",visibility="MESSAGE") adv
#@ boolean Perform_Spatial_Analysis(description="<html><b>If ticked, it will perform spatial analysis for all markers. Convenient than performing them individually. -> </b><html>")
#@ boolean Finetune_Detection_Parameters(description="<html><b>Enter custom rescaling factor and probabilities</b><html>")
#@ boolean Contribute_to_GAT(description="<html><b>Contribute to GAT by saving image and masks</b><html>")
scale = 1;
//making calculate neuron subtype true by default
Calculate_Neuron_Subtype = true;
if(Contribute_to_GAT==true)
{
waitForUser("You can contribute to improving GAT by saving images and masks,\nand sharing it so our deep learning models have better accuracy\nGo to 'Help and Support' button under GAT to get in touch");
img_masks_path = getDirectory("Choose a Folder to save the images and masks");
Save_Image_Masks = true;
}
else
{
Save_Image_Masks = false;
}
//error catch if channel name or number is empty
if(Calculate_Neuron_Subtype==true && Enter_channel_details_now==true && marker_names_manual=="NA" || marker_names_manual=="") exit("Neuron subtype analysis selected\nEnter channel name or untick Enter channel details option");
if(Calculate_Neuron_Subtype==true && Enter_channel_details_now==true && marker_no_manual=="NA" || marker_no_manual=="") exit("Neuron subtype analysis selected\nEnter channel numbers or untick Enter channel details option");
//listing parameters being used for GAT
print("Using parameters\nSegmentation pixel size:"+training_pixel_size+"\nMax neuron area (microns): "+neuron_area_limit+"\nMin Neuron Area (microns): "+neuron_seg_lower_limit+"\nMin marker area (microns): "+neuron_lower_limit);
print("**Neuron\nProbability: "+probability+"\nOverlap threshold: "+overlap);
//print("**Neuron subtype\nProbability: "+probability_subtype+"\nOverlap threshold: "+overlap_subtype+"\n");
//use channel name sort code here from ~line 705
//custom probability for subtypes
//create dialog box based on number of markers
if(Finetune_Detection_Parameters==true)
{
print("Using manual probability and overlap threshold for detection");
Dialog.create("Advanced Parameters");
Dialog.addMessage("Default values shown below will be used if no changes are made");
Dialog.addNumber("Rescaling Factor", scale, 3, 8, "")
Dialog.addSlider("Probability of detecting neurons (Hu)", 0, 1,probability);
//add checkbox to same row as slider
Dialog.addToSameRow();
Dialog.addCheckbox("Custom ROI", 0);
Dialog.addSlider("Overlap threshold", 0, 1,overlap);
Dialog.show();
scale = Dialog.getNumber();
probability= Dialog.getNumber();
custom_roi_hu = Dialog.getCheckbox();
overlap= Dialog.getNumber();
}
else
{ //assign probability subtype default values to all of them
custom_roi_hu =false;
}
if(image_already_open==true)
{
waitForUser("Select Image to analyze");
file_name_full=getTitle(); //get file name without extension (.lif)
selectWindow(file_name_full);
close_other_images = getBoolean("Close any other open images?", "Close others", "Keep other images open");
if(close_other_images) close("\\Others");
dir=getDirectory("Choose Output Folder");
}
else
{
if(endsWith(path, ".czi")) run("Bio-Formats", "open=["+path+"] color_mode=Composite rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT");
else if (endsWith(path, ".lif"))
{
run("Bio-Formats Macro Extensions");
Ext.setId(path);
Ext.getSeriesCount(seriesCount);
print("Opening lif file, detected series count of "+seriesCount+". Leave options in bioformats importer unticked");
open(path);
}
else if (endsWith(path, ".tif")|| endsWith(path, ".tiff")) open(path);
else exit("File type not recognised. GAT is compatible with Tif, Lif and Czi files.");
dir=File.directory;
file_name_full=File.nameWithoutExtension; //get file name without extension (.lif)
}
file_name_length=lengthOf(file_name_full); //length of filename
//if delimiters such as , ; or _ are there in file name, split string and join with underscore
file_name_split = split(file_name_full,",;_-");
file_name_full =String.join(file_name_split,"_");
//Create results directory with file name in "analysis"
analysis_dir= dir+"Analysis"+fs;
if (!File.exists(analysis_dir)) File.makeDirectory(analysis_dir);
//check if it exists
save_location_exists = 1;
do
{
if(file_name_length>50 ||save_location_exists == 1)
{
print("Filename will be shortened if its too long");
file_name_full=substring(file_name_full, 0, 20); //Restricting file name length as in Windows long path names can cause errors
if(save_location_exists == 1)
{
dialog_title = "Save location already exists";
dialog_message_1 = "Save location exists, use a custom identifier.\n For example, writing '_1' as the custom identifier \n will name the final folder as ImageName_1";
}
else
{
dialog_title = "Filename too long";
dialog_message = "Shortening it to 20 characters.\n Use a custom identifier. For example, writing '_1' as the custom identifier \n will name the final folder as ImageName_1";
}
Dialog.create(dialog_title);
Dialog.addString("Custom Identifier", "_1");
Dialog.addMessage(dialog_message_1);
Dialog.show();
suffix = Dialog.getString();
file_name = file_name_full+suffix;
save_location_exists = 0;
}
else file_name=file_name_full;
results_dir=analysis_dir+file_name+fs; //directory to save images
//if file exists in location, create one and set save_location_exists flag to zero to exit the loop
if (!File.exists(results_dir))
{
File.makeDirectory(results_dir); //create directory to save results file
save_location_exists = 0;
}
else
{
waitForUser("The save folder already exists, enter a new name in next prompt");
save_location_exists = 1;
}
}
while(save_location_exists==1)
print("Analysing: "+file_name);
print("Files will be saved at: "+results_dir);
img_name=getTitle();
Stack.getDimensions(width, height, sizeC, sizeZ, frames);
run("Select None");
run("Remove Overlay");
getPixelSize(unit, pixelWidth, pixelHeight);
//Check image properties************
//Check if RGB
if (bitDepth()==24)
{
print("Image type is RGB. It is NOT recommended to\nconvert the image to RGB. Instead, use the raw \noutput from the microscope (which is usually in 8,12 or 16-bit)\n.");
rgb_prompt = getBoolean("Image is RGB. It is recommended to use 8,12 or 16-bit images. Would you like to try converting to 8-bit and proceed?", "Convert to 8-bit", "No, stop analysis");
if(rgb_prompt ==1)
{
print("Converting to 8-bit");
selectWindow(img_name);
run("8-bit");
}
else exit("User terminated analysis as Image is RGB.");
}
//check if unit is microns or micron
unit=String.trim(unit);
if(unit!="microns" && unit!="micron" && unit!="um" )
{
print("Image is not calibrated in microns. This is required for accurate segmentation");
exit("Image must have pixel size in microns.\nTo fix this: Go to Image -> Properties: And enter the correct pixel size in microns.\nYou can get this information from the microscope settings.\nCannot proceed: STOPPING Analysis");
}
//************
//Define scale factor to be used
target_pixel_size= training_pixel_size/scale;
scale_factor = pixelWidth/target_pixel_size;
if(scale_factor<1.001 && scale_factor>1) scale_factor=1;
//do not include cells greater than 1000 micron in area
//neuron_area_limit=1500; //microns
neuron_max_pixels=neuron_area_limit/pixelWidth; //convert micron to pixels
//using limit when segmenting neurons
//neuron_seg_lower_limit=90;//microns
neuron_seg_lower_limit=neuron_seg_lower_limit/pixelWidth;
//using limit for marker multiplication and delineation
//neuron_lower_limit= 160;//microns
neuron_min_pixels=neuron_lower_limit/pixelWidth; //convert micron to pixels
backgrnd_radius=backgrnd_radius/pixelWidth;//convert micron to pixels
//print(neuron_max_pixels,neuron_seg_lower_limit,neuron_min_pixels,backgrnd_radius);
table_name="Analysis_"+file_name;
Table.create(table_name);//Final Results Table
row=0; //row counter for the table
image_counter=0;
//verify neuron channel and ganglia channel
if(cell_channel!="NA")
{
cell_channel=parseInt(cell_channel);
if(isNaN(cell_channel)) exit("Enter which channel number to use for "+cell_type+" segmentation. If leaving empty, type NA in the value");
}
if(ganglia_channel!="NA")
{
ganglia_channel=parseInt(ganglia_channel);
if(isNaN(ganglia_channel)) exit("Enter which channel number to use for Ganglia segmentation. If leaving empty, type NA in the value");
}
//getting channel names and numbers as an array
//if user hasn't entered channel details earlier and wants to
marker_subtype=Calculate_Neuron_Subtype;
//get marker names and channels
if(marker_subtype==1)
{
//user can enter markers beforehand or manually define it here
arr=Array.getSequence(sizeC);
arr=add_value_array(arr,1);
//get channel details from user entered at the start
if(Enter_channel_details_now==true)
{
if(marker_names_manual!="NA")
{ //print(marker_names_manual);
marker_names_manual=split(marker_names_manual, ",");
//trim space from names
marker_names_manual=trim_space_arr(marker_names_manual);
//Array.show(marker_names_manual);
marker_no_manual=split(marker_no_manual, ",");
if(marker_names_manual.length!=marker_no_manual.length) exit("The number of marker names does not match the number of marker channels. Check the entry and retry");
if(marker_names_manual.length>1) //delete Hu from channel list as we are not using it for marker classification
{
//find index of cell_channel;; keep it as string
idx_Hu=find_str_array(marker_no_manual,cell_channel);
if(idx_Hu!="NA") //if Hu found in the channel entries, delete that corresponding channel
{
marker_names_manual=Array.deleteIndex(marker_names_manual, idx_Hu);
marker_no_manual=Array.deleteIndex(marker_no_manual,idx_Hu);
}
}
channel_names=marker_names_manual;//split(marker_names_manual, ",");
channel_numbers=marker_no_manual;//split(marker_no_manual, ",");
channel_numbers=convert_array_int(marker_no_manual);
no_markers=channel_names.length;
//Array.show(channel_names);
//Array.show(channel_numbers);
}
else exit("Marker names not defined");
}
else
//get channel info from user with dialog boxes
{
waitForUser("Define the channel names and numbers for analysis in the next prompt");
no_markers=getNumber("How many markers would you like to analyse?", 1);
string=getString("Enter names of markers separated by comma (,)", "Names");
channel_names=split(string, ",");
if(channel_names.length!=no_markers) exit("The number of marker names does not match the number of marker channels. Check the entry and retry");
channel_numbers=newArray(sizeC);
marker_label_img=newArray(sizeC);
Dialog.create("Select Channels for each Marker");
for(i=0;i<no_markers;i++)
{
Dialog.addChoice("Choose Channel for "+channel_names[i], arr, arr[0]);
//Dialog.addCheckbox("Determine if expression is high or low", false);
}
Dialog.show();
for(i=0;i<no_markers;i++)
{
channel_numbers[i]=Dialog.getChoice();
//hi_lo[i]=Dialog.getCheckbox();
}
}
//once markern names and numbers are defined,
probability_subtype_arr=newArray(channel_names.length);
custom_roi_subtype_arr=newArray(channel_names.length);
if(Finetune_Detection_Parameters==true)
{
print("Enter the probability and overlap threshold for neuronal subtype identification");
Dialog.create("Advanced Parameters (Subtype)");
Dialog.addMessage("The default values selected below will be used if no changes are made");
for ( i = 0; i < channel_names.length; i++)
{
Dialog.addSlider("Probability for "+channel_names[i], 0, 1,probability_subtype);
Dialog.addToSameRow();
Dialog.addCheckbox("Custom ROI", 0);
}
Dialog.addSlider("Overlap threshold", 0, 1,overlap);
Dialog.show();
for ( i = 0; i < channel_names.length; i++)
{
probability_subtype_arr[i]= Dialog.getNumber();
custom_roi_subtype_arr[i]=Dialog.getCheckbox();
}
overlap_subtype= Dialog.getNumber();
}
else
{ //assign probability subtype default values to all of them
for ( i = 0; i < channel_names.length; i++)
{
probability_subtype_arr[i]=probability_subtype;
custom_roi_subtype_arr[i]=false;
}
}
print("**Neuron subtype\nProbability for");
Array.print(channel_names);
Array.print(probability_subtype_arr);
print("Overlap threshold: "+overlap_subtype+"\n");
}
channel_list = Array.getSequence(sizeC);
//add 1 to every value so channel no starts at 1
channel_list = add_value_array(channel_list,1);
//ganglia segmentation options; get channels if user didn't enter
if(sizeC>1 && Ganglia_detection!="Define ganglia using Hu")
{
if (Cell_counts_per_ganglia==true && cell_channel=="NA" && ganglia_channel=="NA") //count cells per ganglia but don't know channels for ganglia or neuron
{
waitForUser("Note the channels for neuron and ganglia and enter in the next box.");
//get active channel
Stack.getPosition(active_channel, active_slice, active_frame);
Dialog.create("Choose channels for "+cell_type);
Dialog.addChoice("Enter which channel to use for "+cell_type+" segmentation", channel_list, active_channel);
Dialog.addChoice("Enter which channel to use for ganglia segmentation", channel_list, active_channel);
//Dialog.addNumber("Enter which channel to use for "+cell_type+" segmentation", 3);
//Dialog.addNumber("Enter which channel to use for ganglia segmentation", 2);
Dialog.show();
cell_channel= parseInt(Dialog.getChoice());//Dialog.getNumber();
ganglia_channel=parseInt(Dialog.getChoice());//Dialog.getNumber();
Stack.setChannel(cell_channel);
resetMinAndMax();
Stack.setChannel(ganglia_channel);
resetMinAndMax();
}
else if(Cell_counts_per_ganglia==true && cell_channel!="NA" && ganglia_channel=="NA") //count cells per ganglia but don't know channels for ganglia
{
waitForUser("Note the channels for ganglia segmentation and enter in the next box.");
//get active channel
Stack.getPosition(active_channel, active_slice, active_frame);
Dialog.create("Choose channel for ganglia");
Dialog.addChoice("Enter which channel to use for ganglia segmentation", channel_list, active_channel);
Dialog.show();
//cell_channel= Dialog.getNumber();
ganglia_channel=parseInt(Dialog.getChoice());//Dialog.getNumber();
//Stack.setChannel(cell_channel);
//resetMinAndMax();
Stack.setChannel(ganglia_channel);
resetMinAndMax();
}
else if(Cell_counts_per_ganglia==true && cell_channel=="NA" && ganglia_channel!="NA") //count cells per ganglia but don't know channels for neuron
{
waitForUser("Note the channels for "+cell_type+" and enter in the next box.");
//get active channel
Stack.getPosition(active_channel, active_slice, active_frame);
Dialog.create("Choose channel for "+cell_type);
Dialog.addChoice("Enter which channel to use for "+cell_type+" segmentation", channel_list, active_channel);
Dialog.show();
cell_channel= parseInt(Dialog.getChoice());//Dialog.getNumber();
Stack.setChannel(cell_channel);
resetMinAndMax();
}
}
else if(Ganglia_detection=="Define ganglia using Hu" && cell_channel=="NA")
{
waitForUser("Enter which channel to use for BOTH "+cell_type+" and ganglia segmentation in the next prompt.");
//get active channel
Stack.getPosition(active_channel, active_slice, active_frame);
Dialog.create("Choose the channel for "+cell_type+" and ganglia segmentation");
Dialog.addChoice("Enter which channel to use for BOTH "+cell_type+" and GANGLIA segmentation", channel_list, active_channel);
Dialog.show();
cell_channel= parseInt(Dialog.getChoice());//Dialog.getNumber();
ganglia_channel = cell_channel;
Stack.setChannel(cell_channel);
resetMinAndMax();
}
else if(Ganglia_detection=="Define ganglia using Hu") ganglia_channel = cell_channel;
else exit("The image needs to have multiple channels, only one channel was found");
//added option for extended depth of field projection for widefield images
if(sizeZ>1)
{
print(img_name+" is a stack");
roiManager("reset");
waitForUser("Verify the type of image projection you'd like (MIP or Extended depth of field\nYou can select in the next prompt.");
projection_method=getBoolean("3D stack detected. Which projection method would you like?", "Maximum Intensity Projection", "Extended Depth of Field (Variance)");
if(projection_method==1)
{
waitForUser("Note the starting and ending slice number of the Z-stack.\nThe slices used to create a Maximum Intensity Projection\ncan be defined in the next prompt.\nPress OK when ready");
Dialog.create("Choose Z-slices");
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
{
max_projection=extended_depth_proj(img_name);
}
}
else
{
print(img_name+" has only one slice, using as max projection");
max_projection=getTitle();
}
max_save_name="MAX_"+file_name;
selectWindow(max_projection);
rename(max_save_name);
max_projection = max_save_name;
selectWindow(max_projection);
run("Select None");
run("Remove Overlay");
//if more than one channel, set on cell_channel or reference channel
if(sizeC>1)
{
Stack.setChannel(cell_channel);
}
roiManager("show none");
run("Duplicate...", "title="+cell_type+"_segmentation");
seg_image=getTitle();
roiManager("reset");
//even if importing custom rois still calculating rescaling as we need rescaled pixelwidth and height later
//calculate no. of tiles
new_width=round(width*scale_factor);
new_height=round(height*scale_factor);
n_tiles=4;
if(new_width>2000 || new_height>2000) n_tiles=5;
if(new_width>4500 || new_height>4500) n_tiles=8;
if (new_width>9000 || new_height>9000) n_tiles=16;
if (new_width>15000 || new_height>15000) n_tiles=24;
print("No. of tiles: "+n_tiles);
//scale image if scaling factor is not equal to 1
if(scale_factor!=1)
{
selectWindow(seg_image);
new_width=round(width*scale_factor);
new_height=round(height*scale_factor);
run("Scale...", "x=- y=- width="+new_width+" height="+new_height+" interpolation=None create title=img_resize");
close(seg_image);
selectWindow("img_resize");
seg_image=getTitle();
getPixelSize(unit, rescaled_pixelWidth, rescaled_pixelHeight);
}
else
{
rescaled_pixelWidth=pixelWidth;
rescaled_pixelHeight=pixelHeight;
}
roiManager("UseNames", "false");
selectWindow("Log");
//if custom ROIs for Hu, import ROI here
if(custom_roi_hu)
{
print("Importing ROIs for Hu");
custom_hu_roi_path = File.openDialog("Choose custom ROI for Hu");
roiManager("open", custom_hu_roi_path);
}
else
{
//Segment Neurons with StarDist
print("*********Segmenting cells using StarDist********");
//segment neurons using StarDist model
segment_cells(max_projection,seg_image,neuron_model_path,n_tiles,width,height,scale_factor,neuron_seg_lower_limit,probability,overlap);
}
//if cell count zero, check with user if they want to terminate the analysis
cell_count=roiManager("count");
if(cell_count == 0)
{
print("No cells detected using StarDist");
proceed = getBoolean("NO cells detected using StarDist, do you still want to continue the analysis?");
if(!proceed)
{
print("Analysis stopped as no cells detected");
exit("Analysis stopped as no cells detected");
}
}
roiManager("deselect");
roi_location = results_dir+cell_type+"_unmodified_ROIs_"+file_name+".zip";
roiManager("save",roi_location);
print("Saved unmodified ROIs from GAT detection at "+roi_location);
selectWindow(max_projection);
roiManager("UseNames", "false");
//roiManager("show all without labels");
roiManager("show all");
//manually correct or verify if needed
waitForUser("Correct "+cell_type+" ROIs if needed. You can add or delete ROIs using the ROI Manager");
cell_count=roiManager("count");
wait(5);
//rename rois
args=cell_type;
runMacro(rename_rois,args);
roiManager("deselect");
selectWindow(max_projection);
//uses roi to label macro code
runMacro(roi_to_label);
wait(5);
//original scaling
neuron_label_image=getTitle();
//using this image to detect neuron subtypes by label overlap
selectWindow(neuron_label_image);
max_save_name="MAX_"+file_name;
saveAs("Tiff", results_dir+cell_type+"_label_"+max_save_name);
rename("Neuron_label"); //saving the file will change the name, so renaming it and getting image name again
neuron_label_image=getTitle();
selectWindow(neuron_label_image);
run("Select None");
print("No of "+cell_type+" in "+max_projection+" : "+cell_count);
//select all rois in roi manager
//selection_indexes = Array.getSequence(roiManager("count"));
///roiManager("select", selection_indexes);
//group_id = 1;
//set_all_rois_group_id(group_id);
//roiManager("deselect");
roi_location_cell=results_dir+cell_type+"_ROIs_"+file_name+".zip";
roiManager("save",roi_location_cell);
//save composite image with roi overlay
args = max_projection+","+results_dir+","+cell_type;
runMacro(save_composite_img,args);
selectWindow(table_name);
Table.set("File name",row,file_name);
Table.set("Total "+cell_type, row, cell_count); //set total count of neurons after nos analysis if nos selected
Table.update;
//Segment ganglia
selectWindow(max_projection);
run("Select None");
run("Remove Overlay");
if (Cell_counts_per_ganglia==true)
{
ganglia_seg_complete = false; //flag for ganglia segmentation QC checking
//do while statement that checks if ganglia binary image occupies greater than 85% of image
//If so, issue a warning and ask if user would like to select a different ganglia seg option
do
{
ganglia_roi_path=""; //ganglia_roi_path and batch_mode arguments not used here for now, but keeping it here for consistency with analyse_neurons macro
batch_mode=false;
ganglia_binary = ganglia_segment(Ganglia_detection,max_projection, cell_channel, neuron_label_image, ganglia_channel,pixelWidth,ganglia_roi_path,batch_mode);
//get area fraction of ganglia_binary.
selectWindow(ganglia_binary);
run("Select None");
area_fraction = getValue("%Area");
if(area_fraction>=85)
{
waitForUser("Ganglia covers >85% of image. If ganglia segmentation\nisn't accurate, click Retry to select a different option\n in the next prompt");
ganglia_seg_complete = getBoolean("Is Ganglia segmentation accurate? If so, click Continue", "Continue", "Retry");
}
else ganglia_seg_complete=true;
//choose another ganglia segmentation option and redo
if(ganglia_seg_complete==false)
{
Ganglia_detection="DeepImageJ";
print("Redoing ganglia segmentation as "+Ganglia_detection+" option was not satisfactory");
Dialog.create("Redo ganglia segmentation\nChoose ganglia segmentation option");
ganglia_seg_options=newArray("DeepImageJ","Define ganglia using Hu","Manually draw ganglia","Import custom ROI");
Dialog.addRadioButtonGroup("Ganglia segmentation:", ganglia_seg_options, 4, 1, "DeepImageJ");
Dialog.show();
Ganglia_detection = Dialog.getRadioButton();
print("Ganglia detection option chosen: "+Ganglia_detection);
}
}
while(ganglia_seg_complete==false)
//get cell count per ganglia
//check if ganglia neuron count and total neuron count match. if not, modify outline
//highlight neurons missing in a separate image; get neuron label image
//
print("Counting the number of "+cell_type+" per ganglia. This may take some time for large images.");
//get cell count per ganglia and returns a table as well as ganglia label window
args=neuron_label_image+","+ganglia_binary;
runMacro(ganglia_cell_count,args);
//label_overlap is the ganglia where each of them are labels
selectWindow("label_overlap");
run("Select None");
selectWindow("cells_ganglia_count");
cell_count_per_ganglia=Table.getColumn("Cell counts");
//check if neuron count per ganglia matches total neuron count;
sum_cells_ganglia = sum_arr_values(cell_count_per_ganglia);
if(sum_cells_ganglia!=cell_count)
{
print("No. of neurons in ganglia ("+sum_cells_ganglia+") does not match the total neurons detected ("+cell_count+").\nThis means that the ganglia outlines are not accurate and missing neurons");
print("Using neuron detection to optimise the ganglia outline");
close(ganglia_binary);//getting new ganglia binary from script
selectWindow("cells_ganglia_count");
run("Close");
neuron_dilate_px = 6.5/pixelWidth; //using 6.5 micron for dilating cells
args=neuron_label_image+",label_overlap,"+neuron_seg_lower_limit+","+neuron_dilate_px;
//return modified ganglia_binary image
runMacro(ganglia_fix_missing_neurons,args);
selectWindow("ganglia_binary");
ganglia_binary = getTitle();
args=neuron_label_image+","+ganglia_binary;
print("Re-trying counting the number of "+cell_type+" per ganglia");
//get cell count per ganglia and returns a table as well as ganglia label window
runMacro(ganglia_cell_count,args);
//label_overlap is the ganglia where each of them are labels
selectWindow("label_overlap");
run("Select None");
selectWindow("cells_ganglia_count");
cell_count_per_ganglia=Table.getColumn("Cell counts");
sum_cells_ganglia = sum_arr_values(cell_count_per_ganglia);
print("No. of neurons in ganglia ("+sum_cells_ganglia+"). Total neurons detected: "+cell_count+")");
}
//label_overlap is the ganglia where each of them are labels
selectWindow("label_overlap");
run("Select None");
run("Duplicate...", "title=ganglia_label_img");
//using this for neuronal subtype analysis
ganglia_label_img = "ganglia_label_img";
//make ganglia binary image with ganglia having atleast 1 neuron
selectWindow("label_overlap");
//getMinAndMax(min, max);
setThreshold(1, 65535);
run("Convert to Mask");
resetMinAndMax;
close(ganglia_binary);
selectWindow("label_overlap");
rename("ganglia_binary");
selectWindow("ganglia_binary");
ganglia_binary=getTitle();
//get row indexes were neuron count per ganglia is zero.
//idx_zero_arr = get_zero_indices(cell_count_per_ganglia);
//cell_count_per_ganglia=Array.deleteValue(cell_count_per_ganglia, 0);
roiManager("deselect");
ganglia_number=roiManager("count");
wait(5);
//rename rois
runMacro(rename_rois,"Ganglia");
//save composite image with ganglia overlay
args = max_projection+","+results_dir+",Ganglia";
runMacro(save_composite_img,args);
roi_location=results_dir+"Ganglia_ROIs_"+file_name+".zip";
roiManager("save",roi_location );
roiManager("reset");
selectWindow(table_name);
Table.set("No of ganglia",0, ganglia_number);
Table.setColumn("Neuron counts per ganglia", cell_count_per_ganglia);
Table.update;
selectWindow("cells_ganglia_count");
run("Close");
}
else ganglia_binary = "NA";
//save images and masks if user selects to save them for Hu and ganglia
if(Save_Image_Masks == true)
{
//print("Saving Image and Masks");
if (!File.exists(img_masks_path)) File.makeDirectory(img_masks_path); //create directory to save img masks
cells_img_masks_path = img_masks_path+fs+"Cells"+fs;
if (!File.exists(cells_img_masks_path)) File.makeDirectory(cells_img_masks_path); //create directory to save img masks for cells
save_img_mask_macro_path = gat_dir+fs+"save_img_mask.ijm";
args=max_projection+","+neuron_label_image+","+"Hu,"+cells_img_masks_path;
//save img masks for cells
runMacro(save_img_mask_macro_path,args);
//ganglia save
if (Cell_counts_per_ganglia==true)
{
ganglia_img = create_ganglia_img(max_projection,ganglia_channel,cell_channel);
ganglia_img_masks_path = img_masks_path+fs+"Ganglia"+fs;
if (!File.exists(ganglia_img_masks_path)) File.makeDirectory(ganglia_img_masks_path); //create directory to save img masks
args=ganglia_img+","+ganglia_binary+","+"ganglia,"+ganglia_img_masks_path;
runMacro(save_img_mask_macro_path,args);
}
}
//spatial analysis for Hu (gets no of neighbours around each neuron (Hu).
if(Perform_Spatial_Analysis==true)
{
Dialog.create("Spatial Analysis Parameters");
Dialog.addSlider("Cell expansion distance (microns)", 0.0, 20.0, 6.5);
Dialog.addCheckbox("Save parametric image/s?", true);
Dialog.show();
label_dilation= Dialog.getNumber();
save_parametric_image = Dialog.getCheckbox();
args=cell_type+","+neuron_label_image+","+ganglia_binary+","+results_dir+","+label_dilation+","+save_parametric_image+","+pixelWidth+","+roi_location_cell;
runMacro(spatial_single_cell_type,args);
//save centroids of rois; this can be used for spatial analysis
selectWindow(neuron_label_image);
setVoxelSize(pixelWidth, pixelHeight, 1, unit);
args=results_dir+","+cell_type+","+roi_location_cell;
runMacro(save_centroids,args);
print("Spatial Analysis for "+cell_type+" complete");
}
//neuron_subtype_matrix=0;
//no_markers=0;
//if user wants to enter markers before hand, can do that at the start
//otherwise, option to enter them manually here
if(marker_subtype==1)
{
if(no_markers>1)
{
channel_combinations=combinations(channel_names); //get all possible combinations and adds an underscore between name labels if multiple markers
channel_combinations=sort_marker_array(channel_combinations);
}
else
{
channel_combinations=channel_names; // pass single combination
}
channel_position=newArray();
marker_label_arr=newArray(); //store names of label images generated from StarDist
selectWindow(max_projection);
Stack.setDisplayMode("color");
row=0;
//array to store the channel names displayed in table
display_ch_names = newArray();
for(i=0;i<channel_combinations.length;i++)
{
print("Counting the number of cells positive for each marker: "+channel_combinations[i]);
//get channel names as an array
channel_arr=split(channel_combinations[i], ",");
if(channel_arr.length==1) //if first marker or if only one marker combination
{
if(no_markers==1) //if only one marker
{
//channel_position=channel_numbers[i];
channel_no=channel_numbers[i];
channel_name=channel_names[i];
probability_subtype_val = probability_subtype_arr[i];
}
else //multiple markers
{
//find position index of the channel by searching for name
channel_idx=find_str_array(channel_names,channel_combinations[i]); //find channel the marker is in by searching for the name in array with name combinations
//use index of position to get the channel number and the channel name
channel_no=channel_numbers[channel_idx]; //idx fro above is returned as 0 indexed, so using this indx to find channel no