Recipe inputs and outputs for Multiplexed Cell Detection and their descriptions are summarized in the table below.
Name | Description | |
---|---|---|
Inputs | Deep Learning Model | Select a deep learning model to apply before this recipe is applied |
Input Image(s) | Select the cell membrane or nuclear membrane marker channel(s) to be used while detecting cells in this recipe | |
Outputs | Cells | Select the object set to which the results of the detection are outputted. Both 3D meshes and 2D slices of detected cells are created. Defaults to creating a new object set. |
Recipe parameters for Multiplexed Cell Detection and their descriptions are summarized in the table below.
Preset Group | Parameter Name | Min Value | Max Value | Description |
---|---|---|---|---|
Detection | Model Type | 1 | 3 | Specifies the model type that can be applied The available model types are the following: cyto2 yields good results in most tested cases including nuclei and cytoplasm and is the recommended first step. |
Typical Cell Diameter | 0 | 1,000 | Specifies the typical diameter of objects (e.g. cells / nuclei) on the image. This parameter is used for rescaling the image input to the Cellpose detection algorithm | |
Probability Threshold | 0 | 100 | Adjusts the sensitivity of cell/nucleus detection. The threshold is applied to the probability map shown in the Detection preview. Higher values mean lesser number of detected cells or nuclei | |
Image Smoothing Filter Size (excluding Skip Smooth Image only) | 0 | 100 | Specifies the diameter of the filter that is used to smooth the input channel before further processing The available smoothing types are the following:
| |
Partition | Cell Diameter | 0 | 10,000 | Specifies the range of detected cells or nuclei to keep based on their diameters |
Mesh Smoothing Factor | 1 | 10 | Adjusts the amount of smoothing applied to the surface reconstructions of the detected Cells; a lower value will generate surfaces with greater similarity to the input image | |
Z-Stack Area Overlap Threshold (For the “Use standard method” option) | 0.0 | 1.0 | Specifies the intersection over union (IoU) threshold to determine whether to merge masks across Z stacks.
| |
Max Z-Stack Displacement (For the “Use flow-based method” option) | 0 | 1,000 | Specifies the displacement limit between mask centers to determine whether to merge masks across Z stacks.
|
There are two preset groups in the recipe: (1) Detection and (2) Partition. 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.
Standard method
Flow-based method
Two intermediate images and 2D/3D view of detected cells or nuclei are available for detection preview
(a) Combined Image - Preview
Preview of the combined image used for cells or nuclei detection
(b) Probability Map - Preview
Preview of the probability map representing the likelihood of cells/nuclei to be detected
Values of the probability map range from 0 to 100
Standard method probability map
Flow-based method probability map
(c) 3D Meshes - Preview
Preview of 3D meshes of detected cells or nuclei
(d) 2D Slices - Preview
Preview of 2D slices of detected cells or nuclei
2D/3D view of detected cells or nuclei are available for partition preview
Same as “3D Meshes - Preview” and “2D Slices - Preview” in detection preview
Detection result in 2D view
Detection result in 3D view
Related articles appear here based on the labels you select. Click to edit the macro and add or change labels.
|