The guide explains you how to trace dendritic spines using the Spine Tracer operation included in the Tracing module. The operation provides 3 methods (AI-assisted, Use Segments and Use Probability Map) to detect and connect dendritic spines to an already existing trace. This guide focuses on the probability map method.
Detecting dendritic spines (Probability Map)
Introduction
Workflow
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 demo dataset
- To download the demo dataset, click https://demodata.arivis.com/data/arivisPro-DemoData-SamplePipelines-SpineTracer.zip (1).
- 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.
Opening demo dataset
- Click File > Open...
- Select the *.sis file from the Windows Explorer.
- The demo dataset is displayed in the viewer. The dataset is visualized according to the current rendering setting parameters.
Pipeline operations layout
The following operations are part of the pipeline.
Input ROI
This operation allows to select the region of interest (ROI). ROI defines the dataset subarea that will be processed and analyzed by the pipeline.
Input ROI parameters
|
Parameter |
Description |
|
|---|---|---|
|
ROI |
Sets the processing and analysis target space. |
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|
– |
Current View |
The selected Z plane and the viewer area are processed. |
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– |
Current Plane |
The selected Z plane is processed (XY). |
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– |
Current Time Point |
The selected time point is processed (XYZ). |
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– |
Current Image Set |
The complete dataset (XYZ and time) is processed. |
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– |
Custom |
Allows to mix the previous methods. |
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Channels |
Sets the processing and analysis target channels. Selecting a single channel, all the operations in the pipeline will be forced to use it. |
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Scaling |
Sets the scaling of the dataset, which reduces it size. The measurements will not be modified by the scaling factor |
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Crop input data |
Sets the limitation of the data used for the calculation to only the cropped selection defined above. If this option is not selected, the entire image set is used for the calculation. |
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Additional Parameters
Additional settings for Input ROI expand, when you click in the operation.
|
Parameter |
Description |
|---|---|
|
Bounds |
Sets the analysis area edges. The whole XY bounds, the viewing area or a custom space can be applied. |
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Planes |
Sets the analysis planes range. A single plane, a range of planes or the whole stack can be selected. |
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Time Points |
Sets the analysis time points range. A single time pint, a range of time points or the whole movie can be selected. |
Deep Learning Reconstruction
The Deep Learning Reconstruction operation allows you to run an image-to-image operation us-ing a trained DL model on any dataset. It could be used for various tasks such as getting prob-ability map of a segmentation model, image denoising, image enhancement, etc.
Deep Learning Reconstruction parameters
|
Parameter |
Description |
|
|---|---|---|
|
Model |
Set a DL model to be used (ONNX or CZDMODEL/CZANN). The operation will extract all necessary info from the loaded model and update the UI accordingly. Note: Click on |
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|
Channel |
Select the channels that will be used for analysis. Channels are available based on the selected model and its input shape. |
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Outputs |
Sets the output of the Operation. Note: click |
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Additional Parameters
Additional settings for the Deep Learning Reconstruction expand when you click in the operation.
|
Parameter |
Description |
|
|---|---|---|
|
Normalization |
Sets a normalization range to be applied on the data before inference. |
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|
– |
Full range |
Full range uses the intensity range of the input data pixel type, e.g. for 8bit it is [0,255]. |
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– |
Data range |
Data range uses the minimum and maximum values of input data over all timepoints. |
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– |
Data range per timepoint |
Data range per timepoint, uses the minimum and maximum values of input data per timepoint. |
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– |
Manual |
Manual allows users to select any desired range using the picker to be used for all channels and timepoints. |
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Customize model settings |
Select this checkbox to be able to modify specific model parameters. Otherwise, default values are used. |
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– |
Min overlap |
Sets a minimum voxel count for overlapping tiles used in the model. The default values are extracted from the loaded model and are zero if no value has been defined in the metadata. |
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– |
Input range |
Sets the intensity range. The default range is between 0 and 1. Change this range to match the trained model. |
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– |
Prediction range |
Sets the range to match the output of the trained model. The default range is considered to be between 0 and 1. |
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Apply tile blending |
Applies smoother transition between the results of two tiles and reduces the tiling artifacts. |
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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. |
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Method |
Sets the method for tracing neurons. Each has a set of settings which need to be properly adjusted to get the best result. |
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|
– |
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. |
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Threshold |
Sets threshold value as the minimum intensity level of the tubular structures vs. the background. |
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Min. terminal section length |
Sets the minimum branch length to be considered. Any structure smaller than this value will not be segmented. |
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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.
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.