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Detecting cells (Seeded Region Growing)

Introduction

This guide explains you how to detect cells starting from an internal seed using the pipeline with the Seeded Region Growing operation. The purpose of the pipeline is to detect objects edges starting from an internal seed. The seed is grown till it reached the main structures borders according to the defined criteria. The pipeline can be applied to Cells, Nuclei, or any kind of small particles.

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

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.

Activating the Sample Pipeline

In the Shortcut Toolbar, click Analysis Panel .
In the Sample Pipelines list, double-click the Detect Cells Using Seeded Region Growing 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.

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.

Denoising

Set of operations performing noise reduction. Choose the more suitable method based on your sample and define the diameter.

Denoising parameters

Parameter		Description
Channels		Sets the processing and analysis target channel(s).
Method		Sets the denoising algorithm.
–	Bilateral	The Bilateral filtering can reduce the noise in an image while maintaining edges. A bilateral filtering blurs an image using both domain and range neighborhoods.
–	Discrete Gaussian	The Discrete Gaussian filter blurs an image by convolution with a discrete Gaussian kernel. This method is fast, but blurs edges.
–	Flow-driven	The curvature Flow-driven denoising filter is an anisotropic diffusion method used to reduce noise or unwanted detail in images while preserving specific image features.
–	Mean	The Mean filter blurs the image by calculating a new intensity value for each pixel. The new intensity is equal to the average of the intensity values of the pixels in the local neighborhood.
–	Median	The Median filter sets the intensity for each pixel to the median of the intensity values in the local neighborhood. The median is the intensity value and the center of the ordered sequence of all pixels in the local neighborhood.
–	Particle enhancement	The Particle Enhancement Filter can be used to extract bright structures of a certain size from a noisy background. It convolves a given image with a special restoration kernel.
Diameter		Sets the reference objects the reference objects diameter. Note: The filter size is expressed as the smaller objects' diameter of the structures that you want to preserve or enhance. This parameter must be expressed in metric unit. Note: You can measure the diameter directly from the dataset with the Measurement tool.

Seeded Region Growing

Automatic objects detection algorithm based on region growing approach. It uses seeds as starting point for the growing task.

Seeded Region Growing parameters

Seeding

Parameter		Description
Method		Sets the seeding method.
–	Nucleus-based	Detects the bright peaks such as nuclei.
–	Membrane-based	Detects dark peaks inside membrane staining.
Channel		Sets the analysis target channel.
Seeded Detection		Sets the seed threshold percentage. A lower value detects fewer, but bigger seeds. A higher value detects more, but smaller seeds.
Area		Sets the Area, Volume or Diameter seeds range. Only the segments that matches the range are accepted as seeds. The filter can be enabled/disabled. Note: You can measure the diameter directly from the dataset with the Measurement tool.
–	Diameter	Filters the unwanted seeds based on their diameter.
–	Volume	Filters the unwanted seeds based on their volume.
–	Area	Filters the unwanted seeds based on their area.

Region Growing

Parameter		Description
Method		Sets the region growing method.
–	Watershed	Segments bright objects such as cells by growing from seeds to the border of an object.
–	Membrane-based watershed	Segments dark objects such as membrane staining by growing from the seeds to the bright intensity outlines.
Channel		Sets the channel for the selected algorithm.
Threshold		Sets this parameter so that the foreground of the region growing channel contains the objects of interest.
Max. distance		Sets the maximum distance on which the region growing will act. Note: You can measure the distance directly from the dataset with the Measurement tool.

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 add or remove table columns, click Feature Columns...
For more information, refer to the Online Help (F1).

Results in the Viewer

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.