ZEISS Knowledge Base

Introduction

The pipeline purpose is to detect objects, falling in two different classes, using Blob Finder and the Intensity Threshold operator. The detected objects are compared to find partial or total overlap conditions. It can be applied to cells, nuclei or any kind of small particles.

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 https://demodata.arivis.com/data/arivisPro-DemoData-SamplePipelines-3DStack.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 Compartmentalize Cells or Particles 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.

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.
–	Current View	The selected Z plane and the viewer area are processed.
–	Current Plane	The selected Z plane is processed (XY).
–	Current Time Point	The selected time point is processed (XYZ).
–	Current Image Set	The complete dataset (XYZ and time) is processed.
–	Custom	Allows to mix the previous methods. Note: Use the Custom option during the pipeline setting and testing. Set a sub volume (XY, Planes, Time Points, channels) of your dataset on which perform the trial. This will speed up the setting process.
Channels		Sets the processing and analysis target channels. Selecting a single channel, all the operations in the pipeline will be forced to use it.
Scaling		Sets the scaling of the dataset, which reduces it size. The measurements will not be modified by the scaling factor
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.

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.
Planes	Sets the analysis planes range. A single plane, a range of planes or the whole stack can be selected.
Time Points	Sets the analysis time points range. A single time pint, a range of time points or the whole movie can be selected.

Allows the segments detection using one of the available automatic threshold methods.

By default, the Method: Auto is selected. Depending on the selection, different parameters are available.

Parameter		Description
Channel		Sets the processing and analysis target channel.
Method		Sets the threshold algorithm. By default, Auto is selected.
–	Simple	This method uses a single threshold and segments everything below or above it.
–	Range	This method uses two Thresholds to define the range of intensities to segment. Everything outside of this range is neglected.
–	Percentile	This method uses a single threshold and segments all intensities below or above it. The threshold is given as a percentile and is based on the data.
–	Percentile range	This method uses two thresholds to define the range of intensities to segment. Everything outside of this range is neglected. The thresholds are given as percentiles and are based on the data.
–	Auto	This method uses a single threshold and segments all intensities below or above it. The threshold is automatically calculated based on the data.
–	Adaptive mean	This method uses local information around each pixel to set a threshold in relation to the local background. It is based on Niblack's thresholding method.
–	Mean	This method uses local information around each pixel to set a threshold in relation to the local background.
Object type		Sets whether to select light or dark objects, i.e., whether the threshold value should be below or above.
–	Bright	Selects the bright objects. This is good for images with a dark background.
–	Dark	Selects the dark objects. This is good for images with a bright background.
Thresholder		Sets the auto algorithm to be used.
–	Otsu	The algorithm maximizes the inter-class variance.
–	Huang	The fuzzing threshold algorithm using the Shannon's entropy function.
–	Li	The algorithm minimizes the cross-entropy.
–	Yen	The algorithm is a 2-factor criterion-based automatic multilevel thresholding.
Range		Sets the extent of the input data for the automatic threshold calculation.
–	Plane	For each plane of the image set an individual threshold is calculated.
–	Time point	For each time point of the image set an individual threshold is calculated.
–	Image set	For the whole image set only one threshold is calculated.

Blob Finder

Automatic small roundish objects detection algorithm. It uses a local threshold method.

You can use the Outputs of the first operation (Blob Finder spots) as Input in the second operation (Blob Finder spots - filtered by volume).

Blob Finder parameters

Parameter	Description
Channel	Sets the analysis target channel.
Diameter	Sets the reference objects diameter (max). Note: Object diameter feature can be measured directly from the dataset with the Measurements tool.
Probability threshold	Sets the probability threshold coefficient. Note: You can use the Picker tool to pick a probability from the preview layer directly.
Split sensitivity	Sets the sensitivity used to split touching objects. Decreasing the Split sensitivity will fuse more objects . Increasing the Split sensitivity will split more objects. Note: You can use the Calculator tool, to compute the split sensitivity.

Object Feature Filter

The previously detected objects are filtered by the selected feature (e.g., Volume). The used criteria can be set from the dropdown list. Multiple features can be set.

Object Feature Filter parameters

Parameter		Description
Input		Sets the filter input. If more than one segment operation is present in your pipeline, the correct input source must be set.
Is of type		Sets the type of filter which filters the objects based on the selected type. By default, it is set on Any for all object types. The list of available features is updated accordingly.
Filter selection		Sets the function for the segmentation is performed.
Criterion Settings		These parameters depend on the selection of the filter.
+ Add Filter		Allows to add a Simple, Ratio or Tag filter criterions.

Compartmentalization

The detected objects' positions are compared to verify its partial or total overlap conditions.

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 step by step

You can execute the pipeline step by step (back and forth). This method allows to run and undo a single operation. You can either use the executing buttons in the Pipeline toolbar or in the Operation toolbar to go through the operation list.

To run the single operation, click .
To undo the single operation, click .
Note: Undo the last operation executed if you need to change the operation settings.

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

Executing buttons in the Operation toolbar after executing

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 show the results of each tag, click Single.
To show the compartment details, click Master-Detail.
Note: You must select the Compartmentalization tag first.
On the top table the list of the reference objects is shown.
To list the subjects belonging to them, click on a single or multiple references.
To add or remove table columns, click Feature Columns...
Note: Use the Master-Detail table to set the features independently for each table.
For more information, refer to the Online Help (F1).

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).

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Compartmentalizing cells or particles