ROI Integration (Online Analysis)

ScanImage® allows to select several regions of interest (ROIs) for live analysis. The pixel intensities within these ROIs is averaged. The resulting time trace is plotted over time in the ROI Integration Display and can be used to drive a digital or analog output.

ROI Integration Experiment Setup


Integration ROIs

An Integration ROI is a 3D object, that defines the geometry of an image feature. See Scanfields, ROIs, ROI Groups to learn more about the ROI concept. The ROI is defined by 2D-cross sections at different z-positions. For each of these slices, a mask can be defined that weighs the underlying pixel values.

../_images/Integration+ROI+with+Mask.png

3D ROI Integration Experiment Setup

Cross-section of an Integration ROI at one z-slice. The ROI contains a weighted mask.


Calculation of Intensity

If no mask is assigned to an integration ROI, the sum of all pixels of the ROI is calculated and is divided by the number of all pixels in the ROI. If the ROI stretches over more than one slice, the sum of all pixels of all slices in the ROI is calculated. The final output is generated when the last slice of the integration ROI is acquired.

If a mask is assigned to an integration ROI, each pixel is multiplied by the associate weight of the mask. Next, the sum of all weighted pixels is calculated. This sum is then divided by the sum of all elements in the mask.

Attention

The ROI integration is done in the Matlab thread. If processing a single frame does not finish before the next frame arrives, Matlab can drop the incoming frame to stay in sync with the acquisition (the frame data is still logged to disk in this case). If this happens, the ROI Integration does not process the dropped frame.


Output File Format

If ROI Integration is enabled in the Integration Control Window, saving is enabled in the Main Controls Window <Main Controls>, and a Grab or Loop is performed, ScanImage® saves the integration value for all integration values into a CSV file, which is located in the same directory as the Tiff file containing the video stream.


Programmatic ROI access

The integration ROIs can be created, modified and deleted using the ScanImage® API. See ROI API to learn more.

Example: Modify the geometry of an Integration ROI
hSI.hIntegrationRoiManager.roiGroup.rois(1).scanfields(1).centerXY = [1,1]; % define the center of the scanfield in scan angle coordinates
hSI.hIntegrationRoiManager.roiGroup.rois(1).scanfields(1).sizeXY = [1,1]; % define the size of the scanfield in scan angle coordinates
hSI.hIntegrationRoiManager.roiGroup.rois(1).scanfields(1).mask = rand(10,10); % define a (random) weighted mask for an integration scanfield

ROI PostProcessing

The IntegrationRoiManager weighs pixel values according to a mask and sums the weighted values to form the integration value. This integration value is displayed without further analysis. However, it might be desired to perform additional computation on the integration values (e.g. to calculate dF/F). This can be done by defining a post processing function.

The IntegrationRoiManager has a property ‘postProcessFcn’. This property defines a function handle points to a function, which, by default, passes the integrationValues through without any processing. By changing this function handle, a different functionality can be achieved.

hSI.hIntegrationRoiManager.postProcessFcn = @myPostProcessingFunction; % overwrite the default value of 'postProcessFcn'

To get started with your own postProcessingFcn, inspect the default post processing function

Default function

1
2
3
4
5
integrationValues = integrationPostProcessingFcn(rois,integrationDone,arrayIndices,integrationValueHistory,integrationTimestampHistory,integrationFrameNumberHistory)
    % This function is used to post process integration values
    % standard behavior: pass the integration values through without changing the calculated values
    integrationValues = integrationValueHistory(arrayIndices);
end

This function can be changed to use the integration history for generating values:

Example: Average last three values

integrationValues = integrationPostProcessingFcn(rois,integrationDone,arrayIndices,integrationValueHistory,integrationTimestampHistory,integrationFrameNumberHistory)
   historyLength = size(integrationValueHistory,1);
   numRois = size(integrationValueHistory,2);

   [is,js] = ind2sub(size(integrationValueHistory),arrayIndices); % get the line and row indices of the current integration values

   is = [is-2;is-1;is]; % % get row indices for last three values
   is(is<1) = historyLength - is(is<1); % integrationValueHistory is a rolling buffer. roll over rowindex over if i < 1
   js = [js;js;js];

   idx = sub2ind(is,js); % get 3xn index matrix
   vals = integrationValueHistory(idx); % get last 3 values for each integration roi

    integrationValues = mean(vals,1); % average last 3 values, return post processed value
end

Warning

The post processing function will be called several times during a volume. Makes sure to optimize the function for performance, so that the the processing pipeline is not slowed down.

Note

only values that are flagged by ‘integrationDone’ actually were updated when the post processing function was called. To optimize for performance, only these values need to be post processed

Performance optimization

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
integrationValues = integrationPostProcessingFcn(rois,integrationDone,arrayIndices,integrationValueHistory,integrationTimestampHistory,integrationFrameNumberHistory)
   historyLength = size(integrationValueHistory,1);
   numRois = size(integrationValueHistory,2);


   arrayIndices_ = arrayIndices(integrationDone); % get arrayIndices for values that changed
   [is,js] = ind2sub(size(integrationValueHistory),arrayIndices_); % get the line and row indices of the new values

   is = [is-2;is-1;is]; % % get row indices for last three values
   is(is<1) = historyLength - is(is<1); % integrationValueHistory is a rolling buffer. roll over rowindex over if i < 1
   js = [js;js;js];

   idx = sub2ind(is,js); % get 3xn index matrix
   vals = integrationValueHistory(idx); % get last 3 values for each integration roi

   integrationValues = nan(1,numRois);
   integrationValues(integrationDone) = mean(vals,1); % only update relevant values
end

Application example

Application idea: overwrite the integrationValue of the last roi to show a peak every 30th frame

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
integrationValues = integrationPostProcessingFcn(rois,integrationDone,arrayIndices,integrationValueHistory,integrationTimestampHistory,integrationFrameNumberHistory)
    integrationValues = integrationValueHistory(arrayIndices);
    % overwrite value for last integration roi
    frameNumber = integrationFrameNumber(arrayIndices(end));
    tfmultiple30 = mod(frameNumber,30)==0;
    if tfmultiple30
        integrationValues(end) = Inf;
    else
        integrationValues(end) = 0;
    end
end

Motion Correction

If the sample moves during an active acquisition, the pre-defined Integration ROIs might not process the correct portion of the image. To ensure the Integration ROIs track the sample, enable ScanImage’s Motion Correction feature.


Output Channels

The output generated by the ROI Integration module can be used to drive analog and digital outputs or to call user defined functions. See ROI Integration Output Channels for more information.