TITLE: QUINT Workflow for Fluorescence
AUTHORS: Michael X. Henderson

[DESCRIPTION]
This protocol describes QUINT workflow for fluorescence. It was updated 9/17/2024. It is based on the published QUINT workflow established by Yates and colleagues (PMID: 31849633).

[GUIDELINES]
Purpose

The purpose of this workflow is to enable mouse brain segmentation, registration, and quantification of regional signals. The simplest segmentation is done in QuPath because this program handles whole slide images and has good segmentation algorithms. Registration is done using 3 programs: QuickNII (aligns to a 3D atlas, typically the 2017 CCFv3 Allen Brain Atlas), VisuAlign (allows for non-linear warp transformation of the atlas to match sections), QMask (masks each side of the brain to allow for bilateral assessment of brain regions). Segmentations and registrations are then brought together in Nutil, enabling the generation of quantitative measures for every region of the brain.

Necessary Programs and Locations

1. QuPath: https://qupath.github.io/
2. DeepSlice: https://www.deepslice.com.au/
3. QuickNII: https://www.nitrc.org/projects/quicknii (SELECT ABA Mouse Edition) 
4. VisuAlign: https://www.nitrc.org/projects/visualign/ 
5. QMask: https://www.nitrc.org/frs/?group_id=1341
               a. Listed as QuickMask.zip 
6. Nutil: https://www.nitrc.org/projects/nutil/
               *QuickNII, VisuAlign, and Nutil can be a bit finicky, and we have found it is best to have these folders on the desktop.

Scanned Slides  

To enable high throughput, slides are scanned on the Axioscan microscope. After slide scanning, images will be found at \\pn.vai.org\projects_primary\henderson\vari-core-generated-data\Axioscan\RNAScope. Move the slides into either the Human or Mouse folder and then into its corresponding stain and channel folder. If a folder does not exist, create one following the naming convention (i.e., Stain3_Slc17a7_Gad1_pS129).

Folder Organization 

The programs for registration and quantification rely on having exact file paths from which to call the data. Therefore, it is easiest to set these folders up from the beginning and using the following layout: 

1. QUINT Workflow 
     a. slide#stain# 
 


       i. QVN (for QuickNII, VisuAlign, Nutil) 
          1. Atlas 
          2. Input 
          3. Mask 
          4. Output_Left 
          5. Output_Right 

[STEPS]
SECTION: QuPath Visualization/Segmentation
1. 
 
Open the QuPath application. Select ‘Create project’.
SECTION: QuPath Visualization/Segmentation
4. Select your slide/stain folder within your QUINT Workflow project folder (i.e., slide24stain19).
SECTION: QuPath Visualization/Segmentation
5. Select ‘Add images’ > ‘Choose files’. Navigate to the image file and select ‘Open’. Set image type to Fluorescence. Select ‘Import’.
SECTION: QuPath Visualization/Segmentation
6. Double click on the first image for it to appear in the viewing window.
SECTION: QuPath Visualization/Segmentation
7. Open the Brightness & contrast window 
 
and leave it open.

SECTION: QuPath Visualization/Segmentation
8. Select ‘Automate’ > ‘Show script editor’. Run the following script (change the channel names to the corresponding channels for your stain). This will change your channel names to the appropriate names. 
 
SECTION: QuPath Visualization/Segmentation
9. Stay on the first image. In the script editor window select ‘Run’ > ‘Run for project’. Move all the images over to the selected column except for the first image. You cannot run a script for the whole project on an image that you have currently open in the viewer. Select ‘OK’. Close the script editor.
SECTION: QuPath Visualization/Segmentation
10. For each image, adjust the Min and Max display in the Brightness & contrast window for each channel as needed to make the scan easier to see. This only changes your visual, no actual properties of the scan. Pathology Stain Segmentation
SECTION: QuPath Visualization/Segmentation
11. Create annotation classifications for any new channel names you have previously not worked with. Select the ‘Annotations’ tab > three dots in the bottom right of the window > populate from image channels. Ensure that the class color matches the channel color.
 
SECTION: QuPath Visualization/Segmentation: Image Export for Registration
12. Select the rectangle button
 
 and select your ROI around the first brain. 
SECTION: QuPath Visualization/Segmentation: Image Export for Registration
13. Lock the rectangle by selecting the annotation (the rectangle will appear yellow). Right click inside the rectangle, select ‘Annotations’ > ‘Lock’. This will lock the rectangle, so it is not accidentally moved.
SECTION: QuPath Visualization/Segmentation: Image Export for Registration
14. MAKE SURE THE RECTANGLE IS SELECTED (YELLOW) AS YOU COMPLETE THIS STEP. Select ‘File’ > ‘Export Images’ > ‘Rendered RBG (with overlays)’. Set the export format to (PNG). Set the downsample factor to 12.0. Select OK.
SECTION: QuPath Visualization/Segmentation: Image Export for Registration
15. Save in the QVN folder with the appropriate naming designation (i.e., sl18st3sc1_s041, sl18st3sc2_s042_).
SECTION: QuPath Visualization/Segmentation: Image Export for Registration
16. Repeat steps 10-13 for each image.
SECTION: QuPath Visualization/Segmentation: Cell Detection
17. Select ‘Analyze’ > ‘Cell Detection’ > ‘Cell Detection’ to open the cell detection window.
SECTION: QuPath Visualization/Segmentation: Cell Detection
18. Adjust the Cell Detection parameters as needed. The most helpful parameters to adjust are Sigma, Minimum Area, Maximum Area, Threshold, and Cell Expansion.

a. Minimum Area and Maximum Area will be dependent on whether you are analyzing mouse or human tissue.
b. Keep all checkboxes checked “Split by Shape”, “Include Cell Nucleus”, “Smooth Boundaries”, and “Make Measurements”.
c. A lower Sigma value will break up nuclei more. A higher Sigma value will combine nuclei more often. 
d. Once you are satisfied with the parameters, you can save the Cell Detection script to 
easily apply it to your other images (‘Workflow’ tab -> Create script). The same cell 
detection parameters must be applied to all images.
SECTION: QuPath Visualization/Segmentation: Cell Detection
19. Remove cells from areas of damaged tissue or outside the brain. 

a. Select scrub tool 
 
 and select button 
 
and drag over areas with cells to remove. Once 
the right cells are highlighted, hit backspace and delete. Turn off select button and 
select move tool
 
to return to normal when finished.
b. Alternatively, Ctrl+click individual cells and delete with backspace.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
20. Select the three dots to the right of ‘Auto Set’ > ‘Add/Remove…’ > ‘Add class’. Name the new class ‘Training’. 
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
21. Choose 6-7 regions across the image set to use as training images. These regions should be 
ones that contain a variety of staining to properly train the classifier. Take regions from 
more than one section get a broad range of what your positive cells look like (like 1-2 
regions per image).

SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
22. For each annotation rectangle created for the training classifier, set its class to ‘Training’ by 
right clicking in the annotation rectangle > ‘Set class’ > ‘Training’. Save (File/Save or Ctrl+S) 
after making final annotation.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
23. Once you have created all your training annotations, select ‘Classify’ > ‘Training images’ > ‘Create training image’. Set the classification of your combined training annotations to ‘Training’ and select ‘Rectangles only’. Set the image type of your combined training annotations to ‘Fluorescence’.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
24. Rename training image (right click on image> rename image) to the channel name you will 
train the classifier to detect. Create a duplicate of the combined training annotations for 
each different channel you have (excluding your nuclear stain). Rename the image to 
include the channel name. To duplicate, right click on the combined training annotation 
image and select ‘Duplicate image(s)’. 
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
25. Draw a rectangle annotation around each training image. Lock the annotation. Run cell detection with the same parameters as your individual images.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
26.  Select ‘Classify’ > ‘Object classification’ > ‘Train object classifier’. Set the object filter to ‘Cells’, the classifier to ‘Artificial neural network (ANN_MLP)’, features to ‘Selected measurements’, classes to ‘All classes’, and training to ‘All annotations’. Select ‘Select’ to the right of ‘Selected measurements’ and select all measurements corresponding to the selected training channel (input channel name in Filter type box and hit select all, apply). Leave the window open. 


 




SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
27. Open the points window
 
Select ‘Add’ two times to add two annotations. Set the first annotation to ‘Ignore*’ by right clicking on the annotation and select ‘Set class’ > ‘Ignore*’. Set the second annotation to the channel you are training.


 


SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
28. In the Brightness & contrast window, turn off all channels except the channel you are training. To better visualize stain, hide detected cells by clicking detection objects button

 

highlight channel of interest and play with channel min/max to optimize stain and eliminate background, show cells by using same button as before.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
29. Train the classifier by using the points annotations to identify cells as either positive for the channel or ignore. Continue training the classifier until you are satisfied with the predicted output. Select live update to view how the points you select are affecting the classifier(place at least 1 point for each class before viewing live update). 
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
30. Give the classifier a name with the slide number, stain number, the channel, and the date (i.e., slide24stain19_Nr4a2_12.16.22). Select ‘Save’.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
31. Repeat steps 24-28 for every training image.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
32. Select ‘Classify’ > ‘Object classification’ > ‘Create composite classifier’. Move the individual classifiers to the ‘selected’ side of the window in the order you want them to be applied. Enter a name for the combined classifier (i.e., sl18st19_composite_11.22.22) and select ‘Save & apply’.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
33. For each whole brain image, select ‘Classify’ > ‘Object classification > ‘Load object classifier’, select the composite classifier you just created and ‘Apply classifier’.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
34. To show sub-classes identified from your composite classifier (cells identified as positive for multiple channels) navigate to the ‘Annotations’ tab > three dots button in the bottom right of the window > ‘Populate from existing objects’ > ‘All classes (including sub-classes)’. Select ‘Yes’ when asked if you want to keep the existing available classes.
SECTION: QuPath Visualization/Segmentation: Training Object Classifiers
35. Write down the 6-digit web color code for each sub-class in your annotation window. You will need this information for Nutil. Double click the channels listed in the annotations tab to view the color code.
 
SECTION: QuPath Visualization/Segmentation: Export Segmentation
36. Navigate to each scene image and rename the annotation that is off the entire brain image. Rename by right-clicking on the selected annotation image > ‘Annotations’ > ‘Set properties’. Rename the annotation using the following naming convention: ‘sl18st19sc1input_s041_’ ; ‘sl18st19sc2input_s042_’.
SECTION: QuPath Visualization/Segmentation: Export Segmentation
38.  Navigate to one of your combined training images.
SECTION: QuPath Visualization/Segmentation: Export Segmentation
39. Select ‘Automate’ > ‘Show script editor’. 
SECTION: QuPath Visualization/Segmentation: Export Segmentation
40. Enter the script in the blue box below at line 1. You can export multiple cell classifications together to simplify the downstream Nutil input. To do so, add more lines with
 
“.addLabel(‘Name of classification’, #).
SECTION: QuPath Visualization/Segmentation: Export Segmentation
41. Select ‘Run’ > ‘Run for project’. Move each scene image over to the ‘Selected’ window and select ‘OK’. 
 

 

SECTION: QuickNII Brain Atlas Registration
61. Open the QuickNII program folder.
SECTION: QuickNII Brain Atlas Registration
62. Open Filebuilder. (FileBuilder loads your registration images by default settings without 
automated alignment, Skip to step 68 if using DeepSlice).
SECTION: QuickNII Brain Atlas Registration
63. Navigate to the QVN folder with the brain image exports from QuPath. These images are not the segmentation exports, but the original brain image exports. These images must all be surrounded by a yellow rectangle.
SECTION: QuickNII Brain Atlas Registration
64. Select all the images to be registered and select ‘Open’. It is useful to add a shortcut of your QUINT Workflow folder to your desktop for simpler navigation.
SECTION: QuickNII Brain Atlas Registration
65. Select ‘Save XML’. Navigate to the QVN folder and save as ‘Filebuilder XML’. Make sure to save this file in the same folder as the brain image exports from QuPath.
SECTION: QuickNII Brain Atlas Registration
66. Close Filebuilder.
SECTION: QuickNII Brain Atlas Registration
67. Open the QuickNII application. Select ‘Manage data’ > ‘Load’ and select the XML file that was just generated in step 66 (DeepSlice: load results.XML from QVN folder). 
SECTION: QuickNII Brain Atlas Registration
68. Double click on the first image in Filebuilder to have it show up in the viewing window in the QuickNII application.

 

SECTION: QuickNII Brain Atlas Registration
69. Select ‘Rainbow 2017’ from the drop-down menu in the upper left-hand corner of the toolbar (1).
SECTION: QuickNII Brain Atlas Registration
70. You can adjust how you see the atlas overlay by dragging the vertical transparency bar on the left side of the screen (2).
SECTION: QuickNII Brain Atlas Registration
71. For the first section, find the anteroposterior position. To do this, drag the sagittal red dot (3) to the correct rostro-caudal position. Select ‘Store’ to save the position.
SECTION: QuickNII Brain Atlas Registration
72. Repeat step 72 for the last section. This will bring all other sections to the approximately correct position.
SECTION: QuickNII Brain Atlas Registration
73. Adjust each individual section to the appropriate place in the atlas. Many adjustments may need to be made until the correct plane of section is identified. 
 

    a. Rotation: clockwise or counterclockwise (4).
    b. Brain size: in the x and y direction (5). 
    c. Rostro-caudal position: adjust sagittal view. 
    d. Left-right plane: adjust horizontal view. 
    e. Front-back plane: adjust sagittal view.

SECTION: QuickNII Brain Atlas Registration
74. Select ‘Store’ before moving off a section or it will not save!
SECTION: QuickNII Brain Atlas Registration
75.  Navigate to the next section by double clicking on the section in the Filebuilder, or by selecting the < and > arrows in the upper-right corner. Edit all sections as noted in step 13
SECTION: QuickNII Brain Atlas Registration
76. Select ‘Manage data’ > ‘Export Propagation’ and save this XML filed as “QuickN XML.xml” within the QVN folder. It is not automatically recognized as a .xml file, hence the need to add “.xml” to the end of the name.
SECTION: QuickNII Brain Atlas Registration
77. Select ‘Save JSON’ and name it “QuickN JSON” within the QVN folder. This JSON file is used for VisuAlign. Make sure to save the JSON file in the same folder as the brain image exports from QuPath and the Filebuilder XML file.
SECTION: QMask
78.  In the QuickNII application, go back to the first section. Show just the rainbow atlas image.
SECTION: QMask
79. Adjust the horizontal plane to a hemispheric split (completely vertical). Ensure proper bisecting by confirming it within the coronal plane. You want to perfectly bisect the coronal plan. Change the angle if needed.
 
SECTION: QMask
80. Hover the cursor over the brain viewing window and record the x-y-z coordinates (shown in the top left of the window) for the following three parts of the brain: top left, top right, and bottom left. 
 
SECTION: QMask
81. Open the QMask tool and select ‘Pick XML’ and open the QuickN XML file generated in QuickNII.
SECTION: QMask
82. Enter the x-y-z coordinates.
SECTION: QMask
83. Select ‘Destination’ and navigate to the Mask folder within your current project folder and select ‘Open’.
SECTION: QMask
84. Select ‘Go’. The mask output should be a black and white PNG. Close QuickNII and the QMask tool. 
 
SECTION: QMask
85. Ensure the mask files are named using the appropriate naming convention (i.e., sl18st3sc1_s041_mask).
SECTION: QMask
86. You can check to see if your mask outputs accurately bisect for a hemispheric split by comparing the mask output to the output from VisuAlign. 
SECTION: VisuAlign
87. Open the VisuAlign application.
SECTION: VisuAlign
88. Select ‘File’ > ‘Open’ and select the QuickN JSON file you created in QuickNII.
SECTION: VisuAlign
89. Drag the opacity bar all the way to the right towards ‘Outline’. This will display an outline of all the regions. This is the easiest format for transformation. You can change the color of the outline and the marker for easier visualization.
SECTION: VisuAlign
90. Align all regions properly.
SECTION: VisuAlign
90.1.  Hover over an atlas region.
SECTION: VisuAlign
90.2. To create a marker, click the space bar. 
SECTION: VisuAlign
90.3. Drag the maker symbol to the correct location. 
 
SECTION: VisuAlign
90.4. Markers can be moved at any point. To delete a maker, hover over the maker and click delete on the keyboard.
SECTION: VisuAlign
91. Select the < and > arrows at the top-right to navigate between sections. There is no need to save 
or store these as in QuickNII. 
SECTION: VisuAlign
92. Once all images are complete, select ‘File’ > ‘Save as’ and save as ‘VisuAlign JSON’ in the QVN folder. This can be opened again to continue aligning later.
SECTION: VisuAlign
93. Select ‘File’ > ‘Export’ and navigate to the Atlas folder and select ‘Select Folder’. This will export the files needed for Nutil. These are also the images that you can compare to the Mask outputs to determine the accuracy of your hemispheric split.
SECTION: Nutil
94. Open the Nutil application and navigate to the ‘Operation’ tab. 
SECTION: Nutil
95. Select ‘New’. Select ‘Quantifier’ from the drop-down menu and select ‘Ok’.
SECTION: Nutil
96. Select ‘Save’ to save the overall Nutil project (i.e., “sl24st19.nut”).
SECTION: Nutil
97. Name each project using the following naming convention: ‘sl#st#_classifier_left/right’.
SECTION: Nutil
98. Set the ‘Segmentation folder’ to the ‘Input’ folder within the QVN folder.
SECTION: Nutil
99. Set the ‘Brain atlas map folder’ to the ‘Atlas’ folder within the QVN folder.
SECTION: Nutil
100. Select ‘Reference atlas’ to the ‘Allen Mouse Brain 2017’.
SECTION: Nutil
101. Set the ‘XML or JSON anchoring file’ to the ‘VisuAlign JSON.json’ file.
SECTION: Nutil
102. Set the ‘Output folder’ to the ‘Output_left’ or ‘Output_right’ folder within the QVN folder depending on which hemisphere you are running.
SECTION: QuPath Visualization/Segmentation: Export Segmentation
37. Scenes with smaller annotation regions that you used to make your training images will have a second segmentation. Delete the segmentation image that corresponds to the smaller annotation,
SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Pixel Classifier for Florescence
45. Isolate the image display to show only the channel you want to segment for. Optimize stain visualization by playing with display min/max.
SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Pixel Classifier for Florescence
46. Create thresholder: Classify>Pixel Classifier>Create Thresholder
 

a. Set Resolution: We usually use Very High (.69um/px) (Note  Resolution for Nutil processing later in            workflow).
b. Set Channel to channel of interest
c. Leave Prefilter as Gaussian and Smoothing sigma at 0
d. Before playing with the threshold, set Above threshold to the classification that works for you. This can be Positive from the default classes, or you can make your own (3 dots next to auto set in Classes tab>Add/Remove>Add Class –edit color by double clicking  class in classes window). Set Below threshold to Ignore*.
e. Optimize threshold – this value is based on fluorescence intensity as seen in display min/max - usually in the thousands  range. Ensure the classifier is only detecting positive signal and adjust threshold number as necessary.
i. You can utilize the C Classifier button to turn on/off the classifier and scrollbar to adjust opacity of detected signal. 

 

f. Give your classifier a name and Create objects to apply it to the open image
SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Pixel Classifier for Florescence
47. You can apply the classifier you just made to other images by loading the classifier when other images are open: Classify>Pixel Classifier>Load pixel classifier>Choose model>Select made classifier. 

a. You can also generate a workflow by selecting ‘Create script’ at the bottom of the workflow tab. This will turn all the commands executed on the open image into Groovy script. You can batch run this script for other images in your project by going to Run>Run for project and selecting for desired images 

 


 

SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Pixel Classifier for Florescence
48. Export segmentation image using tweaked script from brightfield workflow below. 

a. Note in line 13, (.addLabel(‘Insert class here’, 1)) includes the name of the class given to 
the annotation generated by your classifier. This can be found by selecting the 
annotation and locating Classification in the Measurements box underneath it.
 

 

 


SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Pixel Classifier for Florescence
49. Find exported images in your folder scheme under QuPath\export. The images should be the 
exact same dimensions of the earlier region of interest image you created as a PNG with a 
downsize factor of 12. 

a. The segmentation image should have a white background with segmented pixels in the 
color of the annotation class. (Nutil will ask you to insert the object color as a 6-digit alphanumeric web color code. This can be found by double clicking your annotation’s class in the Classes tab>Selecting Custom Color from Dropdown>Selecting Web

 

SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Pixel Classifier for Florescence
50. Place the image into “Input” folder that will be used in final Nutil step.
3. 
2. 
SECTION: DeepSlice Automated Alignment
55. 


 


 
     
Open DeepSlice in your web browser.
SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Subcellular Segmentation
51. 
 

After running cell detection and object classifiers, open Subcellular spot detection window: Analyze>Cell Detection>Subcellular detection (experimental)

 

SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Subcellular Segmentation
52. To restrict subcellular detection to a specific cell type right click on class for desired cell type(s)>Select objects by classification

a.  Selected class cells should appear yellow with all other cells remaining their 
original color
b. Run the subcell detection command
(if you want to run the subcellular detection for all cells, this step is not necessary)

SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Subcellular Segmentation
51.1. Set the detection threshold to the fluorescence value of your pixel classifier

a. The order of the channels in the display window corresponds to the numbering in the subcellular spot detection window. Set the threshold in the channel number of interest and leave the other two at -1 to ignore.
b. Check split by shape to get individual stain objects within each cell
SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Subcellular Segmentation
51.2. Adjust max spot size (adjust to something significantly larger than a single cell area
SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Subcellular Segmentation
53.  Export subcellular segmentation by running this script adapted from pixel classifier export script (note line 13* include the name of class of subcellular object -> can be obtained by clicking 3 dots next to Auto set in Annotation Classes tab>Populate from existing objects (All classes (including sub-classes)):

 

 

The same principles for file location in fluorescent pixel classifier export applies here.


SECTION: QuPath Visualization/Segmentation: Alternate Segmentations, Subcellular Segmentation
54. Neurite Area: Using the pixel classifier export that detects positive area signal everywhere, and the subcellular detection that detects only in cells, we can get neurite area (positive area outside of cells) by using the post-processing tool, Nutil2Usable. Subtracting subcellular area from total area will leave us with only area outside of cells (aka neurite). There is a protocol in N2U that instructs on how to go about that analysis.

 


SECTION: DeepSlice Automated Alignment
56. Select Choose Files.

a. Upload all images for registration from the QVN folder
SECTION: DeepSlice Automated Alignment
57. Ensure Mouse is selected for species, and Model Ensemble is checked (uses two DeepSlice 
versions to optimize alignment ).

a. Avoid checking Angle Integration (this aligns all your brain sections to the same angle, which is inaccurate when blocks have different cutting angles) 
b. Avoid checking Using Cutting Index (this suggests your sections numbers (_s###) correspond to serial section numbers spaced equally apart) 
c. Optional to allow DeepSlice to use your data to improve the neural network and its predictive accuracy.
SECTION: DeepSlice Automated Alignment
58. Select Submit.
SECTION: DeepSlice Automated Alignment
59. After all sections/slices are processed, press Download XML
SECTION: DeepSlice Automated Alignment
60. Insert the downloaded XML (titled by default ‘results.XML’) into your QVN folder with all your 
images for registration.
SECTION: QuPath Visualization/Segmentation: Export Segmentation
42. Find the exported images in the export folder. The segmentation image should be white with objects in their WEB ID color. The images should be the exact same dimensions of the earlier region of interest image you created as a PNG with a downsize factor of 12. 
SECTION: QuPath Visualization/Segmentation: Export Segmentation
43. Scenes with smaller annotation regions that you used to make your training images will 
have a second segmentation. Delete the segmentation image that corresponds to the 
smaller annotation.
SECTION: QuPath Visualization/Segmentation: Export Segmentation
44. Copy the images to the ‘Input’ folder in the QVN folder. These images will be used in the 
final step of Nutil. Close the QuPath application.
SECTION: VisuAlign
90.5.  Start with the outer alignments first then move inwards. 
SECTION: Nutil
105.      
In the advanced settings, set ‘Custom masks (optional)’ to ‘Yes’. 
SECTION: Nutil
104.  Set ‘Object Splitting’ to ‘No’.

 

SECTION: Nutil
103. Change the ‘Object colour’ to match the WEB ID code for the classifier you are currently 
running.
SECTION: Nutil
106. Set the ‘Custom mask folder’ to the ‘Mask’ folder within the QVN folder.
SECTION: Nutil
107. Set the ‘Custom mask colour’ to either white or black depending on which hemisphere you are 
currently running. For output left, set the mask color to white. For output right, set the mask 
color to black. 
SECTION: Nutil
108. Once all settings are in place, select ‘Start’. 

 

SECTION: Nutil
109.      
Repeat for every classifier combination you have and for each hemisphere, remembering to change any settings to match. Double check that every image has a Nutil output. If outputs are missing, run Nutil again.