The guide explains you to detect neurons structures using the tracing module. This module offers algorithms to automatically segment neurons structures based on signal backbone seeds (local maxima) detection and their linking in a logical tree-like architecture. From the segments, several features are afterward measured.
Detecting neurites
Introduction
Demo dataset
The data set is a multi-dimensional, discrete representation of your real sample volume. It can be structured as a z-series of planes (optical slices) of multiple channels (dyes) in a temporal sequence of time points located at multiple spatial positions. Usually, the dataset shows a single experimental situation. A complete experiment can be composed by several datasets. The datasets are available as graphic files saved in plenty of file formats (standard formats as well as proprietary formats).
The link for the specific demo dataset for this guide is displayed below. All datasets are listed here: https://demodata.arivis.com
Downloading the demo dataset
- To download the demo dataset, click http://demodata.arivis.com/data/arivisVision4D-DemoData-SamplePipelines-Trace.zip.
- The download is starting automatically.
- Create a new folder on your local disk. Move the *.zip file from the download folder inside it.
- Unzip the folder.
Activating the Sample Pipeline
- In the Shortcut Toolbar, click Analysis Panel
.
- In the Sample Pipelines list, double-click the Detect Neurites Pipeline.
- If you have activated a pipeline, it will be replaced by the new one.
You can open the appropriate How to guide. By hovering over a pipeline, this button is displayed. When clicking on it, the option Open How to appears.
Pipeline operations layout
The following operations are part of the pipeline.
Neurite Tracer
Automatic tubular-like structures detection based on the threshold-based reconstruction algorithm.
Neurite Tracer parameters
|
Parameter |
Description |
|
|---|---|---|
|
Channel |
Sets the processing and analysis target channels. |
|
|
Method |
Sets the method for tracing neurons. Each has a set of settings which need to be properly adjusted to get the best result. |
|
|
– |
Threshold-based Reconstructor |
This method uses a threshold to separate the foreground from the background. The foreground is searched for connected pixels. These connected pixels are formed into paths and only paths that form a neuron skeleton structure will be kept and used to create a complete trace. |
|
– |
Probabilistic Reconstructor |
This method calculates the local tubularity of the image data. This tubularity map is searched for seed points. Starting from seed points, using a probability function (Monte-Carlo), trace parts are detected. These trace parts are then merge together to create the complete trace. |
|
Threshold |
Sets threshold value as the minimum intensity level of the tubular structures vs. the background. |
|
|
Min. terminal section length |
Sets the minimum branch length to be considered. Any structure smaller than this value will not be segmented. |
|
Store Objects
Store the detected segments (tag) in the active dataset.
Executing the pipeline
You can execute the pipeline step by step or in a single run. To do this, use the executing buttons in the Pipeline toolbar.
Executing in a single run
As alternative to executing step by step, execute the pipeline in a single run.
- To run the whole pipeline, click
.
- To stop the pipeline execution, click
.
Operation status
When the operation is running, this icon is shown.
When the operation is completed, this icon is shown.
Viewing the results
- If not already visible, open the Objects dialog. In Shortcut Toolbar, click Objects table
.
- Measurements are now visible in the objects table.
- To color the results more meaningfully, click Set Object Style.
- Click Apply to: Traces.
- To select the trace color options, select one item from the Color by list.
For some visualization examples, see Visualization examples. - To set further style settings, set the parameters in the Visualization section.
For some visualization examples, see Visualization examples.
Refer to the Help (F1) for more Information.
Visualization examples
|
Color by structure - Cone |
Color by structure - Approximation |
|
Color by depth - Approximation |
Color by depth - Skeleton |
|
Color by depth - Cone |
Modifying the current pipeline
You can modify the pipeline to adapt to another datasets. Therefore, all the pipeline parameters should be set according to the new dataset features.
Previewing the results
For all operations the preview is available in 2D, for some also in 3D.
- Switch from 2D Viewer
to 4D Viewer
in the Viewer Type Switch.
- To preview the operation results, click Preview
in the Operation toolbar.
Use the Navigator panel in the Panel Sidebar to select the preview z plane and/or time points.
Adjusting the operations
The parameters of each operation are described here: Pipeline operations layout
- You have opened the pipeline.
- Go to the operation you want to change.
- Set the appropriate parameters as desired.
- Execute the pipeline (see Executing the pipeline).
Adding or removing operations
You can add or remove operations from the sample pipeline.
- Click + Add operation...
- The operation list is grouped in four groups by their typology.
- To add the operation to the current pipeline, double-click it. Alternatively, drag & drop the operation to the current Pipeline.
Note: The operation cannot be added during the Pipeline execution. - The operation will be inserted at the end of the group of operations to which it belongs.
Voxel operations are positioned before the segment generation. Store operations are always put at the end of the Pipeline. - To remove an operation, click Close
at the Operation toolbar.