3D Neuron Analysis - EM

The 3D Neuron Analysis - EM recipe in Aivia is designed to segment neuron structures in 3D electron microscopy images and create surface reconstructions of the detected structures as well as dendrite traces/centerlines. It is strongly recommended that you specify a deep learning model for EM segmentation as a preprocessing step for this recipe or otherwise feed a confidence map for neuron structures into the recipe. There are EM segmentation models offered in our model library.

The 3D Neuron Analysis - EM recipe generates dendrite objects with segmented dendrite meshes, while the 3D Neuron Analysis - FL recipe generates cylindrical models of dendrites. Both recipes create dendrite traces/centerlines that can be connected to model dendrite trees.

On this page:

1 Parameters and Presets
- 1.1 Parameters
- 1.2 Presets
2 Tutorial
3 Measurements
4 Related Articles

Parameters and Presets

Parameters

Recipe parameters for 3D Neuron Analysis - EM 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
Detection	Intensity Threshold	0	255 (8-bit) 65,535 (16-bit)	Specifies the minimum intensity for segmentation on the input image or the output of the preprocessing deep learning model applied to the input image, if a model is given; a lower value leads to detection of larger objects as well as regions of higher uncertainty
Detection	Reconstruction Type	Not applicable		Allows the user to specify the 3D reconstruction algorithm to use for segmentation; there are two options: Use Robust Reconstruction uses the robust reconstruction algorithm for segmenting 3D objects based on connectivity between adjacent slices of detection; additionally, the algorithm applies slight smoothing and fill holes to enhance the object morphology; use this option for segmenting objects in a very dense environment. Use 3D Watershed uses the watershed algorithm for segmenting 3D objects based on the intensity gradient of the probability map; use this option for segmenting sparse objects.
Subset Filtering	Min Object Size	0	1 x 10¹²(px³ or µm³)	Specifies the range of objects to be included in the analysis results based on the volumes of the detected objects
Subset Filtering	Smoothing Factor	0	10	Adjusts the amount of smoothing applied to the surface reconstructions of the detected objects; a lower value leads to the generation of surfaces with greater similarity to the input image

Presets

There are two preset groups in the recipe: Detection and Subset Filtering. 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.

Detection

Parameter Name	Low	Medium	High

Parameter Name	Low	Medium	High
Intensity Threshold	51 (8-bit) 13,107 (16-bit)	128 (8-bit) 32,768 (16-bit)	242 (8-bit) 62,258 (16-bit)

Subset Filtering

Parameter Name	Small	Medium	Large

Parameter Name	Small	Medium	Large
Min Object Size	5 (px³ or µm³)	20 (px³ or µm³)	10,000 (px³ or µm³)
Smoothing Factor	1	2	5

Tutorial

Before beginning the tutorial, please download the 3D EM Analysis Demo image as well as this deep learning model based on a 3D U-Net architecture trained on the ISBI 2013 dataset from Kasthuri et al. (If you have run the models installer for Aivia, the previously linked deep learning model is located by default at "C:\Program Files\Leica Microsystems\Models\{version number}\Segmentation_3D_EM_ISBI_2013.aiviadl.") 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, "3DEMAnalysisDemo.tif," into Aivia.
In the Recipe Console, click on the Recipe selection dropdown menu and select the 3D Neuron Analysis - EM recipe.
In the Input and Output section of the Recipe Console, click on the Browse icon for the Deep Learning Model field, navigate to and select the "Segmentation_3D_EM_ISBI_2013.aiviadl" model in the dialog, and then click Open.
Select the Low or Medium preset for the Detection preset group.
Click on the caret to the left of the Subset Filtering preset group to show the preset parameters.
Change the Subset Filtering preset group parameter values to the following:
- Min Object Size: 100,000
- Smoothing Factor: 2
Click the Start button or press the F4 key on your keyboard to begin applying the recipe to the image.

The 3D Neuron Analysis - EM recipe generates a neuron object group containing dendrites with segmented meshes as well as traces/centerlines.

Browse for a deep learning model to use for preprocessing

Results

Output dendrite meshes and traces

Measurements

The 3D Neuron Analysis - EM recipe generates some morphological and count measurements for all detected dendrites. You can add additional measurements to the analysis results using the Measurement Tool in Aivia and explore measurement definitions on the Measurement Definitions page. The measurements generated by the recipe are summarized in the table below.

Measurement	Dendrite Trees	Dendrite Segments

Measurement	Dendrite Trees	Dendrite Segments
Mean Diameter	x	x
Branch Count	x	x
Spine Count	x	x
Branch Angle		x
Total Path Length	x	x
Longest Path Length	x	x
Shortest Path Length	x	x
Branch Order	x	x
Surface Area		x
Volume		x
Average Length		x
Top 5 Percent Length		x
CV of Lengths		x
Average Angle		x