ZEISS Knowledge Base

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-SeededRG_3.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.

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 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.

The following operations are part of the pipeline.

This operation allows to select the region of interest (ROI). ROI defines the dataset subarea that will be processed and analyzed by the pipeline.

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.

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.

Blob Finder

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

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.