Inputs and Outputs
Recipe inputs and outputs for Multiplexed Cell Detection and their descriptions are summarized in the table below.
Name | Description | |
---|---|---|
Inputs | Deep Learning Model | (Optional) Select a deep learning model to apply before this recipe is applied |
Input Nucleus Image | Select the channel which contains the nuclei of cells in the image | |
Input Cell Membrane Images | Select the membrane marker channels to be used while detecting cells in this recipe | |
Outputs | Cells | Select the object set to which the results of the detection are outputted. Defaults to creating a new object set |
Parameters and Presets
Parameters
Recipe parameters for Multiplexed Cell Detection and their descriptions are summarized in the table below.
Preset Group | Parameter Name | Min Value | Max Value | Description |
---|---|---|---|---|
Nucleus Detection | Nucleus Probability Threshold | 0 | 100 | Adjusts the sensitivity of nucleus detection. Higher values mean lesser number of detected cells |
Nucleus Diameter | 0 | 65535 px | Indicates the estimated diameter of typical cells | |
Min Nucleus Size | 0 | 65535 px2 or µm2 | Filters out and ignores nuclei of size smaller than this parameter | |
Nucleus Expansion Distance | 0 | 65535 | If no cell membrane is detected corresponding to a nucleus, the cell membrane is generated by expanding the nuclear membrane by a distance specified by this parameter | |
Cell Membrane Detection | Cell Membrane Probability Threshold | 0 | 100 | Adjusts the sensitivity of cell membrane detection. Higher values mean lesser number of detected cells membranes |
Presets
There are two preset groups in the recipe: Nucleus Detection and Cell Membrane Detection. Each group has three pre-configured parameter groupings to help you get started on the analysis. The default preset values are in the sections to follow.
Tutorial (videos)
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