Detecting cells (Cellpose-based Segmenter)
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-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.
Activating the Sample Pipeline
- In the Shortcut Toolbar, click Analysis Panel
.
- In the Sample Pipelines list, double-click the Detect cells Using Cellpose-based Segmenter 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.
Input ROI parameters
|
Parameter |
Description |
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|---|---|---|
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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 |
|---|---|
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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. |
Cellpose-Based Segmenter
This operation detects automatically cells using pretrained cellpose models.
Cellpose-based Segmenter parameters
|
Parameter |
Description |
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|---|---|---|
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Channel |
Sets the channel where the cells will be segmented. |
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Optional channel |
Sets a second optional channel (e.g., nuclei channel) to support the segmentation. |
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Cellpose model |
Sets a pre-trained cellpose model from the list of built-in models. |
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– |
CP |
Sets a model, which is mainly trained on fluorescent cell images . |
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– |
CPx |
Sets a model, which is trained on wide variety of images. |
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Cyto |
Sets a cytoplasm model, which is trained on one channel cell images (if the image had a nuclear channel). |
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– |
Cyto2 |
Sets a cytoplasm model, which is trained on two channels cell images (if the image had a nuclear channel). |
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– |
Nuclei |
Sets a model, which is trained on two-channel images. The first channel is the channel to segment, and the second channel is always set to an array of zeros. |
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– |
Load external model |
Sets a custom model, which allows you to load and run your trained Cellpose models or any pre-trained Cellpose models not included in the operation. |
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Cell diameter |
Sets value of the diameter of your cells. Alternatively, use the Measure |
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Min area |
Sets the reference objects diameter. Select a value to filter out the objects with smaller area, volume, or diameter. |
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Additional Parameters
Additional settings for Cellpose-based Segmenter expand when you click in the operation.
|
Parameter |
Description |
|---|---|
|
Mask threshold |
Sets the cell probability threshold between -6 and +6 to determine what is a cell and what is background. |
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Mask quality threshold |
Sets the maximum allowable errors (mean square error) of the flows for each detected cell. The filter range is between 0 and 1 to exclude masks with lower quality values. |
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Smooth Cellpose model results |
Sets the Gaussian smoothing process, to refine the output, reduce noise and potentially improve the overall performance of your cell detection application. |
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Normalization percentile |
Sets the normalization range. The default values are 1% to 99%, where everything below the lower threshold and everything above the upper threshold is mapped out as outliers. Note:Increase the range if you have data with big background areas and very little area of actual bright signal |
Store Objects
This operation stores 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.
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.
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.