Segmentation Options


This defines the radius of local maxima search, which then serves as the kernel for a region growing algorithm. Decreasing the radius may improve the separation of clumped cells.
Nuclear Stain Cycle
Reference marker to be used for segmentation
Nuclear Stain Channel
Reference marker to be used for segmentation
Nuclear Stain Cycle and Channel
These parameters denote the cycle and channel of the reference nuclear stain on which segmentation should be performed. Though the nuclear stain is repeated on every cycle for purposes of cycle alignment, it is recommended to choose one that contains the best contrast and least amount of saturation. The signal in this channel should be uniform throughout the nucleus, as opposed to the membrane markers which are only visible on the membrane. By default, nuclear cycle and channel are set to 2 and 1.
Use Membrane Stain
This option enables adding an additional marker or stain to the segmentation input. Enable this option only if there is an applicable marker. Depending on the marker, only certain cell types may be helped by this option. The membrane signal is combined with that of the nuclear stain to increase the segmentation volume and hopefully capture more of the biomarker signal. By default, this option is turned off.
Membrane Stain Cycle
Reference marker used to improve cell segmentation
Membrane Stain Channel
​Reference marker used to improve cell segmentation
Membrane Cycle and Channel
These parameters denote the cycle and channel of the reference membrane stain on which segmentation should be performed. These values are only used when membrane stain option is enabled. By default, membrane cycle and channel are set to 1 and 1.
Number of Concentric Circles
​This value determines the required number of concentric circles for a cell volume to be valid. Increasing this value will remove small and irregularly shaped cells that do not exhibit enough circularity. On the other hand, too high of a value may cause cells to drop from segmentation. By default, this parameter is set to 0, i.e. turned off.
Maximum Cutoff
Maximum threshold for local maxima search
Minimum Cutoff
Minimum threshold for local maxima search
Minimum Cutoff ~ Maximum Cutoff
From the peak, the cell volume is grown by adding neighboring voxels whose intensities are between the minimum and maximum cutoff. For example, for 16-bit images, the range is 0 ~ 65,535, so the cutoff range would be 1,310 ~ 64,879. These thresholds aim to eliminate overexposure or low signal indistinct from the background. By default, minimum and maximum cutoff are set to 2% and 99%.
Relative Cutoff
​The relative cutoff limits how big a cell volume can grow by setting a lower limit on the signal relative to the peak. For example, if the peak has an intensity of 20,000, no pixel can have an intensity lower than 400. By default, relative cutoff is set to 2%.
Size Cutoff Factor
​This value eliminates cell volumes under this threshold that may be too small to be a true cell. This value is used as a coefficient to the volume with the radius defined above. For example, with a radius of 6 and size cutoff of 0.1, the resulting threshold volume is about 90 voxels, which translates to 4 µm diameter. By default, size cutoff is set to 0.1.
Use Anisotropic Region Growth
This option enables rounding of corners during cell volume growth across z-slices. Growth within the same z-slice is not affected. As such, when extended depth of field is enabled, this option has no impact. By default, this option is turned on.


Cell segmentation is critical for isolating single cell expression data from a tissue matrix. The CODEX Processor uses inputs from various parameters under the segmentation tab to tailor the segmentation to different cell types and signal intensities.
This technical note summarizes the results and experimental procedures used for optimizing the accuracy of CODEX current segmentation algorithm.
For this purpose, a 6-cycle CODEX run in a 3x3-tiles region of human tonsil FFPE tissue section was acquired using different exposure times for DAPI nuclear staining (5, 10 and 20 ms). A region of interest (ROI) of a representative tile was chosen for optimization to allow visual assessment of the segmentation accuracy (Figure 1).
Figure 1: A region is selected for optimizing the segmentation parameters

Changing Parameters Affects Segmentation Outcome

In order to elucidate how different segmentation parameters and exposure times affect the segmentation outcome, different segmentation runs using the CODEX Processor were performed changing the value of only one parameter at a time while keeping all others as default (default values are reported in Figure 2).
Figure 2: Default parameters for segmentation
Segmentation runs were also performed on images of the same ROI obtained with different exposure times using only default values. Figure 3 (A - E) shows the trend of the different parameters with respect to the cell count. It can be observed that the radius, the minimum cutoff and the size cutoff strongly influence the cell count, which tend to decrease as any of these values increase. Conversely, the relative cutoff and differences in exposure times have a negligible effect on the cell count calculated by the segmentation algorithm. The trend observed in the graph is depend on the sample.
Figure 3: Variation of the cell count as a function of one segmentation parameter on the 10ms DAPI image (A - D) and of the exposure time for segmentations with default values (E).

Optimization of Segmentation Parameters

The DAPI image at 10ms exposure time was segmented using different parameters. Examples of segmentations runs be found in the Appendix. Segmentation accuracy was evaluated by visual identification of over- and under-segmented cells and of false segments (see Figure 5). The total cell count was also compared to the one calculated by visual inspection. For this dataset, the radius and the minimum mutoff values were determined to be critical parameters, and the segmentation was improved by using the following parameters:
Minimal Cutoff
Relative Cutoff
Size Cutoff
Overlays between DAPI nuclear staining and segmentation masks obtained with default and optimized values are reported in Figure 4A and 4B, while the trends visually observed and used to evaluate the segmentation accuracy are reported in Figure 5.
Figure 4A: Superposition between DAPI staining and segmentations masks obtained with default parameters
Figure 4B. Superposition between DAPI staining and segmentations masks obtained with optimized parameters.
Figure 5: Cell counts: Comparison of the number of cells obtained by manual count and by segmentation using default and optimal parameters (A) Number of over- and under-segmented cells and number of missed segmentation events using different segmentation settings (B).
Optimal parameters were also used to segment images of DAPI staining with different exposure times, and, analogous to segmentations done with default settings, the difference in exposure time did not cause important variations of the cell counts (Figure 6).
Figure 6: Comparison of cell counts calculated by visual inspection and by applying optimal segmentation parameters determined for 10ms to DAPI images obtained at different exposure times.


This study demonstrates the parameter optimization process for segmentation using the CODEX Processor for the analysis of a human FFPE tonsil tissue section. While default parameter settings might be sufficient for some tissue types, altered parameters are often required to minimize inaccurate segmentation events including over-segmentation, under-segmentation and missed segmentation events.
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Changing Parameters Affects Segmentation Outcome
Optimization of Segmentation Parameters