(Aivia 12.1) 3D Neuron Analysis - FL

The 3D Neuron Analysis - FL recipe in Aivia detects and automatically traces dendrites in fluorescence 3D volumetric images. The recipe has options for tracing dendrites with or without somas as well as options for automatic detection of dendritic spines. After applying the recipe, you can extend the analysis by applying a neuron classifier to classify the detected neurons.

For tracing neurons in 3D electron microscopy images, use the 3D Neuron Analysis - EM recipe.

After Aivia 13, new measurements are added for the neuron level:

Intensity measurements for neuron
-Mean, Max, Min for neuron (all components)
-Mean, Max, Min for Neuron (dendrites)
-Mean, Max, Min for Neuron (spines)

After Aivia 12.1, users can

Use Cellpose⁽¹⁾ model as the soma detector to detect neurons in dense regions.
Choose different input channels for soma detection and dendrite tracing.
Filter out elongated false positive objects

On this page:

1 Automatic Parameter Selection
- 1.1 Auto Icon
- 1.2 Active Tile
2 Parameters and Presets
- 2.1 Input and Output
- 2.2 Parameters
- 2.3 Presets
3 Tutorial
4 Measurements
5 Image Credits
6 Related Articles
7 References

Automatic Parameter Selection

Auto Icon

3D Neuron Analysis - FL features Aivia That Learns: AI-powered automatic parameter selection.

Click on the Auto icon (see right) in the recipe header to initiate parameter prediction, which feeds features from the specified input image channel into a model trained by human experts and then populates the Recipe Console with predicted parameters.

Auto icon for automatic parameter selection

Active Tile

Another way to initiate parameter estimation for 3D Neuron Analysis - FL is through the Neuron Analysis Active Tile, which is accessed from the search bar at the top of the Aivia window. The Neuron Analysis Active Tile also adjusts the Aivia layout to one that is recommended for a neuron analysis recipe workflow. If the Neuron Analysis Active Tile is not shown when you click in the search bar, try clicking on the Expand icon in the lower-left corner of the search dropdown.

Active Tiles

Parameters and Presets

Input and Output

After Aivia 12.1, the recipe can take two different input channels for soma and dendrite/spine detection.

Parameters

Recipe parameters for 3D Neuron Analysis - FL and their descriptions are summarized in the table below.

Preset Group	Parameter Name	Min Value	Max Value	Description

Preset Group	Parameter Name	Min Value	Max Value	Description
Soma Detection	Soma Diameter	0	1,000 (px or µm)	Specifies the average diameter of somas (neuron cell bodies); a lower value leads to detection of smaller objects on the image
	Max Soma Aspect Ratio	0	100	Soma will be removed from output set if the ratio of the major axis to the average of two minor axes exceed the threshold
	Switchable Option: Detect Soma with Cellpose			Using Cellpose based deep learning model as soma detector. Suitable for images with a high density of neurons.
	Seed Detection Intensity Threshold	0	255 (8-bit) 65,535 (16-bit)	This parameters is used to set the minimum soma intensity as a threshold to detect seeds for preview and skip block processing. *For Cellpose for soma detection option, the preview option generates spots for indicating locations where Cellpose will be run for improved speed for preview options but these spots do not 100% correspond to where somas get generated as Cellpose may not detect somas at the location where the preview spots are generated.
	Cell Probability Threshold	0	100	Only probability values above the threshold in the Cellpose predicted probability map will under go further processing to generate soma mask.
	Switchable Option: Detect Soma with Partition			Blob detection with 3D watershed
	Min Soma Intensity Threshold	0	255 (8-bit) 65,535 (16-bit)	Specifies the minimum intensity of somas on the image for partitioning touching somas; this parameter is only enabled when the Enable Soma Partition option is selected; a lower value reduces the number of partitions and preserves larger soma
	Switchable Option: Skip Soma Partition			Skip the blob detection and using Otsu thresholding as soma detector
Dendrite Detection	Dendrite Diameter	0	50 (px or µm)	Specifies the diameter of a typical dendrite on the image; a lower value increases detection sensitivity for thin dendrites and decreases detection sensitivity for thicker dendrites (such as axons)
	Min Branch Length	0	1,000 (px or µm)	Specifies the minimum length of a traced branch to be included in the analysis results
	Intensity Threshold	0	255 (8-bit) 65,535 (16-bit)	Specifies the intensity range for dendrite tracing using the voxel scooping algorithm; the lower threshold specifies the minimum intensity that is not considered background; the upper threshold specifies the minimum intensity above which the voxels are always considered as part of a dendrite
	Mean Intensity Offset	0	255 (8-bit) 65,535 (16-bit)	Adjusts the detection sensitivity of the dendrite tracing algorithm; a higher value reduces the number of dendrites detected that are at or near the minimum intensity threshold specified above
	Switchable Option: Skip Soma Detection
	Max Connection Distance	0	1,000 (px or µm)	Specifies the maximum gap distance between dendritic traces to join together; this parameter is only enabled when the Skip Soma Detection option is selected; a lower value increases the number of disconnected dendritic branches
Spine Detection (Enable Spine Detection and Enable Enhanced Spine Detection only)	Typical Spine Head Diameter	0	100 (px or µm)	Specifies the approximate diameter of a typical dendritic spine head; this parameter is only enabled when the Enable Spine Detection option or Enable Enhanced Spine Detection option is selected
	Spine Head Diameter	0	100 (px or µm)	Specifies the size range for a spine to be included in the analysis results based on the diameter of the spine head; this parameter is only enabled when the Enable Spine Detection option or Enable Enhanced Spine Detection option is selected
	Max Spine Neck Length	0	100 (px or µm)	Specifies the maximum distance between spine heads and dendritic branches; this parameter is only enabled when the Enable Spine Detection option or Enable Enhanced Spine Detection option is selected; a lower value reduces the search range for spine heads

Presets

There are three preset groups in the recipe: Soma Detection, Dendrite Detection, and Spine Detection; each group has three pre-configured parameter groupings to help you get started on the analysis. The default preset values are given in the sections to follow.

Soma Detection

Parameter Name	Small	Medium	Large

Parameter Name	Small	Medium	Large
Soma Diameter	10 - 50 (px or µm)	40 - 100 (px or µm)	100 - 300 (px or µm)
Min Intensity Threshold	61 (8-bit) 15,728 (16-bit)	41 (8-bit) 10,486 (16-bit)	10 (8-bit) 2,621 (16-bit)

Dendrite Detection

Parameter Name	Small	Medium	Large

Parameter Name	Small	Medium	Large
Dendrite Diameter	2 (px or µm)	5 (px or µm)	15 (px or µm)
Min Branch Length	10 (px or µm)	20 (px or µm)	50 (px or µm)
Intensity Threshold	10 - 50 (8-bit) 2,621 - 12,845 (16-bit)	40 - 80 (8-bit) 10,289 - 20,578 (16-bit)	60 - 100 (8-bit) 15,401 - 25,690 (16-bit)
Mean Intensity Offset	0	0	0
Max Connection Distance	10 (px or µm)	25 (px or µm)	50 (px or µm)

Spine Detection

Parameter Name	Small	Medium	Large

Parameter Name	Small	Medium	Large
Typical Spine Head Diameter	1 (px or µm)	2 (px or µm)	5 (px or µm)
Spine Head Diameter	0.05 - 4 (px or µm)	0.2 - 10 (px or µm)	1 - 15 (px or µm)
Max Spine Neck Length	2 (px or µm)	2 (px or µm)	2 (px or µm)

Spine Detection is disabled by default. Expand the preset group and select Enable Spine Detection or Enable Enhanced Spine Detection to enable the preset group and view the parameters. With Enable Enhanced Spine Detection, spines are detected with greater accuracy at the expense of processing time.

Tutorial

Before beginning the tutorial, please download the Neuron Analysis Demo image. For information on how to select presets or modify parameter values, please refer to the tutorial on how to use the Recipe Console.

Unzip the demo file and load the demo image, "NeuronAnalysisDemo.tif," into Aivia.
In the Recipe Console, click on the Recipe selection dropdown menu and select the 3D Neuron Analysis - FL recipe.
Click the drop-down arrow icon beside Input and Output
1. Select the Unnamed channel in the demo image as the Input Soma Channel
2. Select the Unnamed channel as the Input Dendrite Channel as well
Click on the Show Advanced Interface icon to expand the Recipe Console and show the parameter options for the recipe.
Modify the parameter values in the recipe as follows for the Soma Detection and Dendrite Detection groups:
- Soma Diameter: 8 - 15
- Max Soma Aspect Ratio: 100
- Select Skip Soma Partition
- Dendrite Diameter: 1.5
- Min Branch Length: 5
- Intensity Threshold: 18 - 200
- Mean Intensity Offset: 0
To enable spine detection, go to Step 6; otherwise, go to Step 8.
Click on the Switch Recipe Operations icon for the Spine Detection group to show the list of available recipe operations; select the Enable Spine Detection option from the dropdown menu.
Select the Small preset for the Spine Detection group.
Click the Start button or press the F4 key on your keyboard to begin applying the recipe to the image.

The recipe generates three output object sets: Soma Set, Dendrite Set and Spine Set. You can toggle the display of individual object sets as well as change their display colors from the Object Set Settings panel.

Results

Analysis results with different colors for the soma, dendrites, and spines

Show Advanced Interface icon

Switch Recipe Operations icon

Measurements

The 3D Neuron Analysis - FL recipe generates morphological, intensity, and count measurements for the somas, dendrites, and spines of each detected neuron. You can add additional measurements to the analysis results by using the Measurement Tool in Aivia and view measurement definitions on the Measurement Definitions page. Select Measurements generated by the recipe are described in the table below.

Object Set	Morphology	Intensity	Count

Object Set	Morphology	Intensity	Count
Soma Set	Surface Area Volume	Mean Intensity Max Intensity Min Intensity Total Intensity Std. Dev. Intensity	None
Dendrite Trees	Mean Diameter Total Path Length Longest Path Length Shortest Path Length	None	Branch Count Spine Count Branch Order
Dendrite Segments	Mean Diameter Branch Angle Total Path Length Longest Path Length Shortest Path Length Surface Area Volume Average Length Top 5 Percent Length CV Lengths Average Angle	Mean Intensity Max Intensity Min Intensity Total Intensity Std. Dev. Intensity	Branch Count Spine Count Branch Order
Spines	Total Spine Length Head Neck Length Ratio Total Spine Volume Total Spine Surface Area Head Neck Volume Ratio Spine Head Diameter Spine Diameter Ratio Length to Head Diameter Ratio Spine Surface Area Ratio Spine Convex Hull Volume Ratio Spine Tortuosity Branch Angle	None	None
Spine Heads	Surface Area Volume Average Length Top 5 Percent Length CV Lengths Average Angle	Mean Intensity Max Intensity Min Intensity Total Intensity Std. Dev. Intensity	None
Spine Necks	Surface Area Volume Average Length Top 5 Percent Length CV Lengths Average Angle	Mean Intensity Max Intensity Min Intensity Total Intensity Std. Dev. Intensity	None
Neurons	Min Max, Mean, Total Dendrite Branch Mean Diameter Min, Max, Mean, Total Dendrite Branch Path Length Min, Max, Mean, Total Dendrite Branch Surface Area Min, Max, Mean, Total Dendrite Branch Volume Min, Max, Mean, Total Dendrite Branch Tortuosity Min Max, Mean, Total Dendrite Branch Local Bifurcation Angle Min, Max, Mean, Total Dendrite Branch Partition Asymmetry Min, Max, Mean, Total Dendrite Branch Path Length to Root	Mean, Max, Min for neuron (all components) Mean, Max, Min for Neuron (dendrites) Mean, Max, Min for Neuron (spines)	Dendrite Count Nunber of Bifurcation Number of Branches Number of Terminal Tips Min, Max, Mean, Total Dendrite Branch Node Count Min, Max, Mean, Toital Dendrite Branch Generation

Image Credits

Maryann Martone and Eric Bushong, Cell Image Library (CIL:48401), http://www.cellimagelibrary.org/images/48401

References

Stringer C, Wang T, Michaelos M, and Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nature Methods. 18: 100-106. (2021)

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