Contents

Installation

1. Link in New Window
   linkText Download FIJI href https://imagej.net/software/fiji/downloads

    for your operating system

2. Link in New Window
   linkText Download QC Scope (QC_Scope.jar) href https://github.com/nstifani/QC_Scope/raw/refs/heads/main/QC_Scope.jar

3. Unzip FIJI.app to your favorite location on your computer (usually your Desktop

   Link in New Window
   icon false linkText not in a system directory) href https://imagej.net/software/fiji/downloads

4. Start FIJI (aka FIJI.app, ImageJ-win.exe)
5. Drag and drop QC_Scope.jar into the FIJI toolbar
6. Click Save
7. Click OK
8. Close FIJI

9. Reopen FIJI

After installation, QC Scope will be available in the FIJI menu under Plugins>QC Scope.

QC Scope Toolbar

The QC Scope floating toolbar is available for rapid and convenient access. To load it, click the QC Scope Toolbar in the FIJI menu under Plugins>QC Scope>QC Scope Toolbar.

QC Scope Auto-start

Additionally and for an even more convenient usage, it is possible to have the QC Scope Toolbar automatically loaded when FIJI is started. Click the Auto-start button or select the QC Scope Toolbar Auto-start in the FIJI menu under Plugins>QC Scope>QC Scope Toolbar Auto-start.

QC Scope Demo Images

For testing purposes, you will find 

Field Uniformity

Usage

1. Open FIJI.
2. Launch the QC Scope Toolbar by navigating to Plugins>QC Scope>QC Scope Toolbar.
3. Click on Uniformity.
   1. If one or more images are already opened QC Scope will process them.
   2. If no image is open, QC Scope will prompt to select a folder and process all images withing the folder (and subfolders)
4. QC Scope will try to read the Metadata from the first image and pre-process all the channels with default or the last used processing settings
5. It will display the metadata, the initial results and the processing options in a dialog
6. Check the metadata, the results and the binned image. If it looks good you can click OK. 

7. QC Scope will save files in a folder named Output on your desktop:

   1. At least 2 CSV files:

      1. Field Uniformity_All-Data_Merged.csv gathers all the measured parameters
         Field Uniformity_All-Data_Merged.csv
         

      2. Field Uniformity_Essential-Data_Merged.csv gathers only the essential information
         Field Uniformity_Essential-Data_Merged.csv
         

   2. Optionally, if Save Individual Files is selected QC Scope will also save:
      1. 1 CSV file per image ImageName_Uniformity-Data.csv with one row per channel containing all the measured parameters
      2. 1 TIF file per channel for every processed image showing the binned (Iso-density or Iso-Intensity) map with the Reference Center indicated as an overlay

 Settings

• Microscope Settings read from the image metadata or from the preferences: Objective Magnification (character), NA (Numeric>0), and Immersion media 
• Image Settings: Pixel Width (Numeric>0), Height (Numeric>0), Depth (Voxel) (Numeric>0), Unit (character). QC Scope uses standard space unit (nm, um, mm, cm, m, in, pixels) and will try to convert the entered value into on of those.
• Channel Settings: For each channel: Name (character) and Emission Wavelength in nm ((Numeric>0)) as well as the source of the values
• Processing Settings:
  • Binning Method:
    • Iso-Density (preferred): This method divides the image into 10 bins of equal Nb of Pixels. Nb Pixel Per Bin = (Width x Height) / 10 and assign a new pixel value of 25 for all the Nb Pixel Per Bin darkest pixels, 50 for the next darkest Nb Pixel Per Bin etc... until 250 for the brightest Nb Pixel Per Bin pixels.
    • Iso-Intensity: This method divides the image into 10 bins of equal bin intensities. Bin Width = (Max - Min) / 10 and assign a new pixel value of 25 for all the pixels with an intensity between Min and Min + Bin With, 50 for intensities between Min + Bin Width and Min + 2 x Bin Width etc... until 250 for intensities between Min + 9 x Bin Width and Max.
  • Gaussian Blur: Apply a Gaussian blur with the given Gaussian Blur Sigma before processing each channel
  • Channel: The selected channel will be processed with the entered processing parameters and displayed as part of the testing process to define optimal processing parameters.
  • Batch Mode: If activated, QC Scope will re-use the settings without displaying the dialog unless metadata differs
  • Save Individual Files: For each image, QC Scope will save the individual processed images (1 per channel) and a CSV file with all measured parameters.
  • Prolix Mode: Display all the QC Scope actions in the log

  • Test Processing: When selected the QC Scope Field Uniformity Dialog will keep appearing. This is useful to test the Processing Settings. When you are satisfied you can uncheck Test processing to proceed to the next image.

Metrics

 Field Uniformity measures how evenly illumination is distributed and how centered is the illumination across the field of view. The following metrics provide different perspectives on uniformity:

1. Uniformity: Evaluates the overall evenness of illumination. Since a single value can't capture all variations, QC Scope computes several uniformity metrics for a comprehensive assessment.

   1. Standard Deviation (GV): The standard deviation of pixel intensities measures how much individual pixel values deviate from the mean intensity of the image. A low standard deviation indicates that the pixel intensities are close to the mean, suggesting high uniformity and minimal variation across the image. The standard deviation is in the same unit as the pixel intensity values in the image (Grey Value).

   2. Uniformity Std (%): Calculated as ratio between the intensities of the darkest and brightest pixels. This metric evaluates image uniformity by comparing the minimum pixel intensity to the maximum pixel intensity in the image. It ranges between 0 and 1. A value of 1 indicates perfect uniformity, where the minimum and maximum intensities are equal. This simple metric provides a quick indication of how evenly distributed the pixel intensities are. It highlights whether there are extreme intensity variations, such as bright or dark spots, which can suggest uneven illumination or artifacts. While easy to compute, this ratio is sensitive to outliers. A single very bright or very dark pixel can skew the measurement. It also does not take in account the distribution of pixel intensities and instead focuses on the minimun and maximum.
      In MetroloJ_QC, the Field Uniformity function applies a Gaussian blur before calculation. This reduce the impact of potential outliers. QC Scope uses the unmodified image to compute the Uniformity. For this reason QC Scope will always provide lower Uniformity Std values compared to MetroloJ QC as it is representing the worse case scenario.

      LaTeX Formatting
      $$\text{Uniformity}_{Std} = \frac{\text{Min}}{\text{Max}}$$

       

   3. Uniformity Percentile (%): This metric evaluates image uniformity by analyzing the average intensities of the darkest and brightest pixels within a chosen percentile (e.g., 5%) instead of relying on extreme values. It uses the mean intensities of these percentile subsets to provide a more robust and representative measure of uniformity.

      LaTeX Formatting
      $$\text{Uniformity}_{Percentile} = \left(1 - \frac{\text{Avg}_{95} - \text{Avg}_{5}}{\text{Avg}_{95} + \text{Avg}_{5}} \right)$$

   4. Coefficient of Variation (CV): The Coefficient of Variation is a standard statistical measure used to quantify the relative variation or dispersion of pixel intensities in an image. It is calculated as the ratio of the Standard Deviation to the Mean of the pixel intensities. The CV provides a dimensionless measure of variation, making it easier to compare variability across images with different intensity scales or ranges. It reflects the extent of variability in relation to the average intensity. A lower CV indicates higher uniformity, with pixel intensities tightly clustered around the mean. The CV adjusts for differences in intensity scale, enabling fair comparisons between images. It is particularly useful in imaging workflows where intensity levels may vary across samples. CV is an excellent metric as long as the mean is not close to 0.

      LaTeX Formatting
      $$\text{CV} = \frac{\text{Standard Deviation}}{\text{Mean}}$$

   5. Uniformity_CV (%): This metric is derived from the Coefficient of Variation (CV) and is tailored for microscopy quality control, where users aim to capture highly uniform images. It assumes low variation (CV≪1) and provides an intuitive measure of uniformity expressed as a percentage. A perfectly uniform image, with CV = 0 results in a uniformity of 100%, while increasing variability lowers the value. A uniformity of 0% corresponds to CV = 1, where the standard deviation equals the mean intensity, indicating significant intensity variation. This metric is particularly useful in controlled imaging conditions typical of microscopy quality control. It assumes low CV value. Uniformity_CV offers a simple robust and user-friendly way to assess image uniformity. 

      LaTeX Formatting
      $$\text{Uniformity}_{CV} = (1 - \text{CV})$$

2. Centering Accuracy (%): Assesses how close the brightest region is to the image center. Defined as:

   LaTeX Formatting
   $$\text{Centering Accuracy} = 1 - \frac{2}{\sqrt{\text{Image Width}^2 + \text{Image Height}^2}} \times \sqrt{(\text{X}_{\text{Ref Pix}} - \frac{\text{Image Width}}{2})^2 + (\text{Y}_{\text{Ref Pix}} - \frac{\text{Image Height}}{2})^2}$$

3. Field Illumination Index: Combine both Uniformity_CV and the Centering Accuracy in a single measure. A field Illuminatin index of 100% indicates a perfectly uniform and centered image.
   

   LaTeX Formatting
   $$\text{Field Illumination Index} = \text{Weigth}\times \text{Uniformity}_{CV} + (1 - \text{Weight})\times \text{Centering Accuracy}$$

QC Scope Field Uniformity Metrics (in bold the variables included in Essential Data)

Results

• Convert the Field Uniformity_Essential-Data_Merged.csv created by QC Scope Field Uniformity function into a .xlsx
• Summarize the data with a pivot table
• Use the provided spreadsheet template Field Uniformity_Template.xlsx
  
• Enter your measurement into the highlighted cells of the template to visualize your results. The cells in grey are automatically computed.
  
• Plot the Field illumination Index for each objective and filter combination

The 2x, 20x and 63x objectives have a field illumination index above 70% for all filters indicating an acceptable uniformity and centering accuracy. The 10x objective field illumination index is below 70% indicating a poor uniformity and/or Centering accuracy requiring further investigations.

• Plot the Uniformity and Centering Accuracy for each objective and filter combination
  

All objectives show a good Uniformity. The 2x, 10x and 20x objectives have a centering accuracy below 70% indicating a poor illumination centering.

Iso-density map showing the localization of the centroid of the highest intensity bin for 10x and 63x objectives.

Conclusion

Centering accuracy could be improved on the 2x and 10x objectives. This is not too much of a concern since Uniformity is well above 70%. 

Channel Alignment

Usage

1. Open FIJI.
2. Launch the QC Scope Toolbar by navigating to Plugins>QC Scope>QC Scope Toolbar.
3. Click on Ch Alignment.
   1. If one or more images are already opened QC Scope will process them.
   2. If no image is open, QC Scope will prompt to select a folder and process all images withing the folder (and subfolders)
4. QC Scope will try to read the Metadata from the first image and pre-process all the channels with the default or the last used processing settings
5. It will display the metadata, the initial results and the processing options in a dialog
6. Check the metadata and the results. If it looks good you can click OK. You can also modify the the metadata and processing options

7. QC Scope will save files in a folder named Output on your desktop:

   1. At least 2 CSV files are saved :

      1. Channel-Alignment_All-Data_Merged.csv gathers all the measured parameters
         Channel Alignment_All-Data_Merged.csv
         

      2. Channel Alignment_Essential-Data_Merged.csv gathers only the essential information
         Channel Alignment_Essential-Data_Merged.csv
         

   2. Optionally, if Save Individual Files is selected QC Scope will also save:
      1. 1 CSV file per image gathering all the measured parameters for each channel pair (Image-Name_Channel-Alignment_All-Data.csv)

Settings

• Microscope Settings: Read from the image metadata or from the preferences: Objective Magnification (character), NA (Numeric>0), and Immersion media 
• Image Settings: Read from the image Calibration. Pixel Width (Numeric>0), Height (Numeric>0), Depth (Voxel) (Numeric>0), Unit (character). QC Scope uses standard space unit (nm, um, mm, cm, m, in, pixels) and will try to convert the entered value into one of those.
• Channel Settings: Read from the image metadata or from the preferences, for each channel: Name (character) and Emission Wavelength in nm ((Numeric>0)) as well as displaying the source of the values
• Processing Settings:
  • Detection Method:
    • Log Detector: Laplacian of Gaussian (LoG) detector. From Trackmate: The LoG detector is the best detector for Gaussian-like particles in the presence of noise. It is based on applying a LoG filter on the image and looking for local maxima. The Laplacian of Gaussian result is obtained by summing the second order spatial derivatives of the gaussian- filtered image, and normalizing for scale. Reference: Trackmate manual (page 52). 
    • Dog Detector: Difference of Gaussian (DoG) detector. From Trackmate: This detector is based on the Difference of Gaussian (DoG) filter. It approximates the Laplacian of Gaussian (LoG) filter with the aim at offering better speed. It is commonly used when applying a collection of DoG filters tuned to a wide range of scales. Reference: Trackmate manual (page 52). 
  • Threshold: This is a quality threshold. Only spots above the quality threshold will be detected. The quality value is larger for : Bright spots, spots which diameter is close to the specified diameter.
  • Diameter: This is the diameter for the detection of spots with the indicated unit. Usually this is the diameter of the beads used for the channel alignment.
  • Median Filtering: If activated QC Scope will ask Trackmate to use a Median Filtering. Median filtering: will apply a 3 x 3 median filter prior to any processing.
    This can help dealing with images that have a marked salt and pepper noise which generates spurious spots. Reference: Trackmate manual (page 11). 
  • Channel: The selected channel will be processed with the entered processing parameters and displayed as part of the testing process to define optimal processing parameters. Selecting an another channel will automatically enable Test Processing.
  • Batch Mode: If activated, QC Scope will re-use the settings without displaying the dialog unless metadata differs or the detection fails.
  • Save Individual Files: If activated, QC Scope will save an additional CSV File per image named Image-Name_Channel-Alignment_All-Data.csv gathering all the measured parameters for each channel pair (1 row per channel pair = Nb Channel² rows per file. 
  • Prolix Mode: Display all the QC Scope actions in the log. If used in combination with Save Individual Files, it will save the original Trackmate spot table (1 per channel)
  • Subpixel Precision: If activated QC Scope will ask Trackmate to use a subpixel localization for the detection of spots

  • Test Processing: When selected the QC Scope Dialog will keep appearing. This is useful to test the Processing Settings. When you are satisfied you can uncheck Test processing to proceed to the next image. Changing the selected channel automatically select the Test Processing option.

• Pre-detection results:
  • Nb of Detected spots: Indicate the number of detected spot per channel. To proceed, exactly one spot must be detected for every channel.
  • Max Quality: Indicate the maximum quality of all detected spots for each channel. The maximum quality can then be used to adjust the threshold value

Metrics

Channel Alignment measures how a single bead appears in different channels. QC Scope Channel Alignment detects one spot per channel and retrieve the xyz coordinates using TrackMate plugin More info about TrackMate. It then computes the distance between two spots and compare it to a reference spot. The position of the reference spot depends on the actual xyz resolution of the system. A Co-localization ratio is then calculated:

$$\text{Colocalisation Ratio} = \frac{\text{Distance}_{\text{Spot Ch1-Spot Ch2}}}{\text{Distance}_{\text{Spot Ch1-Spot Reference}}}$$

• A Colocalization Ratio above 1 indicates that the Spot detected in Channel 2 is further away from the spot detected in Channel 1 than the reference spot. In other words the spot in channel 2 is further away than the resolution of the system. This indicates that Channel Alignment is not good and corrections should be performed using the pixel shift table.
• A Colocalization Ratio below 1 indicates that the spot detected in Channel 2 is closer from the spot detected in channel 1 than the reference spot. In other words the distance between the spots detected in channel 1 and 2 is smaller than the resolution of the system. This ratio indicates a good Channel Alignment.

QC Scope Channel Alignment Metrics (in bold the variables included in Essential Data)

Results

• Convert the Channel Alignment_Essential-Data_Merged.csv created by QC Scope Channel Alignment function into a .xlsx
• Summarize the data with a pivot table
• Use the provided spreadsheet template Channel Alignment_Template.xlsx
  
• Paste the Colocalization Ratio and the XYZ pixel shifts in the Shift Tab. The cells use conditional formatting to highlight cells with a ratio above 1.0.

These results indicates that the 63x objective requires correction for the DAPI channel. User should be informed to correct the images.

63x DAPI (Cyan) and Cy5 (Magenta) 4um bead raw image.

  63x DAPI (Cyan) and Cy5 (Magenta) 4um bead corrected for chromatic shift.