QC Scope is a plugin for FIJI designed to process microscope images for Quality Control purposes.
Written by Nicolas Stifani from the Centre d'Innovation Biomédicale de la Faculté de Médecine de l'Université de Montréal.

Installation

for your operating system
Unzip FIJI.app to your favorite location on your computer (usually your Desktop
Start FIJI (aka FIJI.app, ImageJ-win.exe)
Drag and drop QC_Scope.jar into the FIJI toolbar
Click Save
Click OK
Close FIJI
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 that can be used to process and generate results.

Additional information

For now, QC Scope only processes images with the following extensions ".tif", ".tiff", ".jpg", ".jpeg", ".png", ".czi", ".nd2", ".lif", ".lsm", ".ome.tif", ".ome.tiff"

QC Scope never overwrites files. It checks for the existence of files and increment a number until it can safely write the output file without erasing data.

If you are well organized when saving and naming your files, QC Scope will help you.
It specifically check for the presence of "_" in the filename and split it into Filename Variables.
For example if you label your image as Date_Microscope-A_Objective-X_Filter-Y_Condition-001.TIF QC Scope will provide in the output one column with each variable: Date, Microscope, Objective, Filter, Condition. Then you can use a Pivot Table in Excel to quickly access your data.

Field Uniformity

Usage

Open FIJI.
Launch the QC Scope Toolbar by navigating to Plugins>QC Scope>QC Scope Toolbar.
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)
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
It will display the metadata, the initial results and the processing options in a dialog
Check the metadata, the results and the binned image. If it looks good you can click OK.
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

Example of iso-density image with the coordinates of the reference center.

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.

Microscopie > 2025/01/22 > QC Scope > QC_Scope_Field Uniformity Dialog.png

QC Scope Field Uniformity dialog

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:

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.
  $$\text{Uniformity}_{Std} = \frac{\text{Min}}{\text{Max}}$$
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.
  $$\text{Uniformity}_{Percentile} = \left(1 - \frac{\text{Avg}_{95} - \text{Avg}_{5}}{\text{Avg}_{95} + \text{Avg}_{5}} \right)$$
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.
  $$\text{CV} = \frac{\text{Standard Deviation}}{\text{Mean}}$$
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 () and provides an intuitive measure of uniformity expressed as a percentage. A perfectly uniform image, with results in a uniformity of 100%, while increasing variability lowers the value. A uniformity of 0% corresponds to 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.
  $$\text{Uniformity}_{CV} = (1 - \text{CV})$$

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

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

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

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

Key Order	Field Name	Data Example	Data Type	Description
1	Filename	10x_Quad_Exp-01.czi	String	Name of the processed image
2	Channel Nb	4	Integer	Number of the Channel from 1 to n
3	Channel Name	DAPI	String	Name of the Channel
4	Channel Wavelength EM (nm)	465	Integer	Channel Emission Wavelength
5	Objective Magnification	10x	String	Objective Magnification
6	Objective NA	0.25	Float	Objective Numerical Aperture
7	Objective Immersion Media	Air	String	Objective Immerion Media
8	Gaussian Blur Applied	TRUE	Boolean	If Gaussian Blur was applied
9	Gaussian Sigma	10	Integer	Sigma of the Gaussian Blur
10	Binning Method	Iso-Density	String	Binning Method used
11	Batch Mode	TRUE	Boolean	Boolean key to process images in batch mode (no Dialog)
12	Save Individual Files	FALSE	Boolean	Boolean key to save individual files (1 csv data per file with 1 row per channel, 1 tif binned image par channel)
13	Prolix Mode	FALSE	Boolean	Boolean key display detailed plugin actions in the log
14	Image Min Intensity	856	Integer	Raw Image Minimum of Pixel Intensities
15	Image Max Intensity	1080	Integer	Raw Image Maximum of Pixel Intensities
16	Image Mean Intensity	959.1	Float	Raw Image Mean of Pixel Intensities
17	Image Standard Deviation Intensity	24.3	Float	Raw Image Standard Deviation of Pixel Intensities
18	Image Median Intensity	959	Integer	Raw Image Median Pixel Intensity
19	Image Mode Intensity	110	Integer	Raw Image Mode Pixel Intensity
20	Image Width (pixels)	1388	Integer	Image Width in pixels
21	Image Height (pixels)	1040	Integer	Image Height in pixels
22	Image Bit Depth	16	Integer	Image Bit Depth
23	Pixel Width (um)	0.645	Float	Image pixel width (unit/px)
24	Pixel Height (um)	0.645	Float	Image pixel height (unit/px)
25	Pixel Depth (um)	1	Float	Image voxel depth (unit/voxel)
26	Space Unit	micron	String	Raw Image Space Unit
27	Space Unit Standard	um	String	Standardize Space Unit (nm, um, cm, m)
28	Calibration Status	TRUE	Boolean	Boolean key displaying the calibration status
29	Standard Deviation (GV)	24.3	Float	Raw Image Standard Deviation of Pixel Intensities
30	Uniformity Standard (%)	0.793	Float	Uniformity as calculated by MetroloJ_QC. Uniformity_Standard = (Min / Max)
31	Uniformity Percentile (%)	0.958	Float	Uniformity calculated with the average of the 5% and 95% pixel intensities. Uniformity_Percentile = (1 - (Avg_Intensity₉₅ - Avg_Intensity₅) / (Avg_Intensity₉₅ + Avg_Intensity₅) )
32	Coefficient of Variation	0.0253	Float	Coefficient of variation. CV = (Std_Dev / Mean)
33	Uniformity CV based	0.975	Float	Uniformity calculated from the Coefficient of variation. Uniformity_CV = (1 - CV)
34	X Center (pixels)	694	Integer	Coordinate in pixel of the center of the Image (Ideal centering). Image Width (pixels) / 2
35	Y Center (pixels)	520	Integer	Coordinate in pixel of the center of the Image (Ideal centering). Image Height (pixels) / 2
36	X Ref (pixels)	230.4	Float	Coordinate in pixels of the centroid of the largest particule identified in the last bin. Used to caculate the Centering Accuracy
37	Y Ref (pixels)	876.4	Float	Coordinate in pixels of the centroid of the largest particule identified in the last bin. Used to caculate the Centering Accuracy
38	X Ref (um)	148.6	Float	Coordinate in scaled unit of the centroid of the largest particule identified in the last bin.
39	Y Ref (um)	565.3	Float	Coordinate in scaled unit of the centroid of the largest particule identified in the last bin.
40	Centering Accuracy (%)	0.32	Float	Centering Accuracy = 1 - (2 / sqrt(Image Width2 + Image Height*2)) sqrt ( (X_Ref_Pix - Image Width/2)2 + (Y_Ref_Pix - Image Height/2)2)**
41	Field Illumination Index (%)	0.884	Float	Field Illumination Index = Weight * Uniformity_CV+ (1 - Weight) * Centering Accuracy Weight = 0.5

Uniformity_CV (%): 100% perfectly uniform. 0% or less if the Standard Deviation of pixel intensities in the image is equal or higher than the mean.
Centering Accuracy (%): 100% illumination is perfectly centered on the image. Ratio of the distance of the centroid of the Indicates how the 10% brightest pixels to the center image
Field Illumination Index: Combine both Uniformity_CV and Centering Accuracy in a single Index. 100% indicates a perfectly uniform and centered image.

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

Microscopie > 2025/01/22 > QC Scope > image-2025-1-24_13-20-13.png

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

Microscopie > 2025/01/22 > QC Scope > 10x_Quad_Exp-02_Channel-04_Iso-Density-1.png

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

Field illumination Index for each objective and filter combination

Channel Alignment

Usage

Open FIJI.
Launch the QC Scope Toolbar by navigating to Plugins>QC Scope>QC Scope Toolbar.
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)
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
It will display the metadata, the initial results and the processing options in a dialog
Check the metadata and the results. If it looks good you can click OK. You can also modify the the metadata and processing options
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)

Microscopie > 2025/01/22 > QC Scope > QC_Scope_Ch_Alignment Dialog.png

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

Microscopie > 2025/01/22 > QC Scope > QC_Scope_Ch_Alignment Dialog.png

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)

Key Order	Field Name	Data Example	Data Type	Description
1	Filename	20x_Quad_Exp-01.czi	String	Name of the processed image
2	Objective Magnification	20x	String	Objective Magnification
3	Objective NA	0.5	Float	Objective Numerical Aperture
4	Objective Immersion Media	Air	String	Objective Immerion Media
5	Immersion Media Refractive Index	1.0003	Float	Objective Immerion Media Refractive Index
6	Detection Method	Dog Detector	String	Method for the detection Log Dectector or Dog Detector
7	Spot Diameter (um)	4	Float	Spot Diameter used for the detection
8	Threshold Value	20	Float	Quality threhsold value used for the detection
9	Subpixel Localization	True	Boolean	Subpixel Localization used for the detection
10	Median Filtering	False	Boolean	Median Filtering used for the detection
11	Batch Mode	True	Boolean	Boolean key to process images in batch mode (no Dialog)
12	Save Individual Files	True	Boolean	Boolean key to save individual files (1 csv data per file with 1 row per channel, 1 tif binned image par channel)
13	Prolix Mode	False	Boolean	Boolean key display detailed plugin actions in the log
14	Image Width (pixels)	100	Integer	Image Width in pixels
15	Image Height (pixels)	100	Integer	Image Height in pixels
16	Image Bit Depth	16	Integer	Image Bit Depth
17	Pixel Width (um/px)	0.3225	Float	Image pixel width (unit/px)
18	Pixel Height (um/px)	0.3225	Float	Image pixel height (unit/px)
19	Pixel Depth (um/px)	1.24	Float	Image voxel depth (unit/voxel)
20	Space Unit	micron	String	Raw Image Space Unit
21	Space Unit Standard	um	String	Standardize Space Unit (nm, um, cm, m)
22	Time Unit	sec	String	Time Unit
23	Calibration Status	True	Boolean	Boolean key displaying the calibration status
24	Channel 1	1	Integer	Number of the Channel 1 from 1 to n
25	Name Channel 1	Cy5	String	Name of the Channel 1
26	EM Wavelength Channel 1 (nm)	673	Integer	Channel 1 Emission Wavelength
27	Nb Detected Spots Ch1	1	Integer	Nb of Detected Spots for Channel 1
28	Spot ID Ch1	2055	Integer	Spot ID for Channel 1
29	Spot Quality Ch1	132	Float	Spot Quality for Channel 1
30	X Ch1 (um)	16.005	Float	Coordinate of the center of the detected spot for Channel 1 along the X axis in the indicated unit
31	Y Ch1 (um)	16.311	Float	Coordinate of the center of the detected spot for Channel 1 along the Y axis in the indicated unit
32	Z Ch1 (um)	35.171	Float	Coordinate of the center of the detected spot for Channel 1 along the Z axis in the indicated unit
33	T Ch1 (sec)	0	Float	Time of the detected spot for Channel 1 in the indicated unit
34	Frame Ch1	0	Integer	Frame of the detected spot for Channel 1
35	Radius Ch1 (um)	2	Float	Radius of the detected spot for Channel 1 in the indicated unit
36	Visibility Ch1	True	Boolean	Spot visibility for Channel 1
37	Channel 2	3	Integer	Number of the Channel 2 from 1 to n
38	Name Channel 2	GFP	String	Name of the Channel 2
39	EM Wavelength Channel 2 (nm)	509	Integer	Channel 2 Emission Wavelength
40	Nb Detected Spots Ch2	1	Integer	Nb of Detected Spots for Channel 2
41	Spot ID Ch2	2075	Integer	Spot ID for Channel 2
42	Spot Quality Ch2	132	Float	Spot Quality for Channel 2
43	X Ch2 (um)	16.05	Float	Coordinate of the center of the detected spot for Channel 2 along the X axis in the indicated unit
44	Y Ch2 (um)	16.243	Float	Coordinate of the center of the detected spot for Channel 2 along the Y axis in the indicated unit
45	Z Ch2 (um)	35.718	Float	Coordinate of the center of the detected spot for Channel 2 along the Z axis in the indicated unit
46	T Ch2 (sec)	0	Float	Time of the detected spot for Channel 2 in the indicated unit
47	Frame Ch2	0	Integer	Frame of the detected spot for Channel 2
48	Radius Ch2	2	Float	Radius of the detected spot for Channel 2 in the indicated unit
49	Visibility Ch2	True	Boolean	Spot visibility for Channel 2
50	Channel Pair	Cy5 x GFP	String	Combination of Channel 1 x Channel 2 Names
51	X Shift (um)	0.046	Float	Difference between the coordinates of Channel 2 and Channel 1 along the X axis in the indicated unit
52	Y Shift (um)	-0.068	Float	Difference between the coordinates of Channel 2 and Channel 1 along the Y axis in the indicated unit
53	Z Shift (um)	0.547	Float	Difference between the coordinates of Channel 2 and Channel 1 along the Z axis in the indicated unit
54	X Shift (pixels)	0.1	Float	Difference between the coordinates of Channel 2 and Channel 1 along the X axis in pixels
55	Y Shift (pixels)	-0.2	Float	Difference between the coordinates of Channel 2 and Channel 1 along the Y axis in pixels
56	Z Shift (pixels)	0.4	Float	Difference between the coordinates of Channel 2 and Channel 1 along the Z axis in pixels
57	Distance Lateral (um)	0.082	Float	Lateral distance between the spot detected in Channel 2 and Channel 1 in the indicated unit
58	Distance Axial (um)	0.547	Float	Axial distance between the spot detected in Channel 2 and Channel 1 in the indicated unit
59	Distance 3D (um)	0.553	Float	3D distance between the spot detected in Channel 2 and Channel 1 in the indicated unit
60	Conversion Factor	1000	Float	Conversion factor to convert Emission Wavelength from nm to the same unit than the calibrated image
61	EMWavelength Unit Ch1 (um)	0.673	Float	Emission Wavelength in the indicated unit for Channel 1
62	EMWavelength Unit Ch2 (um)	0.509	Float	Emission Wavelength in the indicated unit for Channel 2
63	Nyquist Pixel Size Lateral Ch1 (um)	0.337	Float	Nyquist Lateral pixel Size for Channel 1 in the indicated unit. Formula : Nyquist_Pixel_Size_Lateral = EMWavelength_Unit / (4 * Objective_NA)
64	Nyquist Pixel Size Axial Ch1 (um)	2.513	Float	Nyquist pixel Axial Size for Channel 1 in the indicated unit. Formula : Nyquist_Pixel_Size_Axial = EMWavelength_Unit / (2 * Refractive_Index * (1-cos(Theta))) with Theta = asin(Objective_NA / float(Refractive_Index))
65	Nyquist Ratio Lateral Ch1	1	Float	Nyquist Lateral Ratio for Channel 1 in the indicated unit. Formula : Nyquist_Ratio_Lateral = Pixel_Width / Nyquist_Pixel_Size_Lateral. A ratio above 1 indicate the Pixel Size does not meet the Nyquist sampling criteria (Pixel too big)
66	Nyquist Ratio Axial Ch1	0.5	Float	Nyquist Axial Ratio for Channel 1 in the indicated unit. Formula : Nyquist_Ratio_Axial = Pixel_Depth / Nyquist_Pixel_Size_Axial. A ratio above 1 indicate the Pixel Depth does not meet the Nyquist sampling criteria (Z Stack steps too big)
67	Nyquist Pixel Size Lateral Ch2 (um)	0.255	Float	Nyquist pixel Size for Channel 1 in the indicated unit. Formula : Nyquist_Pixel_Size_Lateral = EMWavelength_Unit / (4 * Objective_NA)
68	Nyquist Pixel Size Axial Ch2 (um)	1.9	Float	Nyquist Lateral Ratio for Channel 2 in the indicated unit. Formula : Nyquist_Ratio_Lateral = Pixel_Width / Nyquist_Pixel_Size_Lateral. A ratio above 1 indicate the Pixel Size does not meet the Nyquist sampling criteria (Pixel too big)
69	Nyquist Ratio Lateral Ch2	1.3	Float	Nyquist Axial Ratio for Channel 2 in the indicated unit. Formula : Nyquist_Ratio_Axial = Pixel_Depth / Nyquist_Pixel_Size_Axial. A ratio above 1 indicate the Pixel Depth does not meet the Nyquist sampling criteria (Z Stack steps too big)
70	Nyquist Ratio Axial Ch2	0.7	Float	Nyquist pixel Size for Channel 2 in the indicated unit. Formula : Nyquist_Pixel_Size_Lateral = EMWavelength_Unit / (4 * Objective_NA)
71	Resolution Lateral Theoretical Ch1 (um)	0.686	Float	Theoretical Lateral Resolution for Channel 1 in the indicated unit. Formula: Resolution_Lateral_Theoretical = (0.51 * EMWavelength_Unit) / (Objective_NA)
72	Resolution Axial Theoretical Ch1 (um)	4.766	Float	Theoretical Axial Resolution for Channel 1 in the indicated unit. Formula: Resolution_Axial_Theoretical = (1.77 * Refractive_Index * EMWavelength_Unit) / (Objective_NA ** 2)
73	Resolution Lateral Practical Ch1 (um)	0.686	Float	Practical Lateral Resolution for Channel 1 in the indicated unit. If the Nyquist Lateral Ratio is above 1 the Practical Lateral Resolution = Theoretical Lateral Resolution x Nyquist Lateral Ratio otherwise the Practical Lateral Resolution = Theoretical Lateral Resolution
74	Resolution Axial Practical Ch1 (um)	4.766	Float	Practical Axial Resolution for Channel 1 in the indicated unit. If the Nyquist Axial Ratio is above 1 the Practical Axial Resolution = Theoretical Axial Resolution x Nyquist Axial Ratio otherwise the Practical Axial Resolution = Theoretical Axial Resolution
75	Resolution Lateral Theoretical Ch2 (um)	0.519	Float	Theoretical Lateral Resolution for Channel 2 in the indicated unit. Formula: Resolution_Lateral_Theoretical = (0.51 * EMWavelength_Unit) / (Objective_NA)
76	Resolution Axial Theoretical Ch2 (um)	3.605	Float	Theoretical Axial Resolution for Channel 2 in the indicated unit. Formula: Resolution_Axial_Theoretical = (1.77 * Refractive_Index * EMWavelength_Unit) / (Objective_NA ** 2)
77	Resolution Lateral Practical Ch2 (um)	0.658	Float	Practical Lateral Resolution for Channel 2 in the indicated unit. If the Nyquist Lateral Ratio is above 1 the Practical Lateral Resolution = Theoretical Lateral Resolution x Nyquist Lateral Ratio otherwise the Practical Lateral Resolution = Theoretical Lateral Resolution
78	Resolution Axial Practical Ch2 (um)	3.605	Float	Practical Axial Resolution for Channel 2 in the indicated unit. If the Nyquist Axial Ratio is above 1 the Practical Axial Resolution = Theoretical Axial Resolution x Nyquist Axial Ratio otherwise the Practical Axial Resolution = Theoretical Axial Resolution
79	X Ref (um)	16.143	Float	Coordinate in indicated unit of the reference spot in the along the X axis. The reference spot is a calculated as a spot at the interesect between the Line defined by Spot Channel 1 and Spot Channel 2 coordinates and an ellipse centered on Spot Channel 1 with Distance Lateral Ref as a semi-minor axis and Distance Axial Ref as a semi-major axis
80	Y Ref (um)	16.106	Float	Coordinate in indicated unit of the reference spot in the along the Y axis. The reference spot is a calculated as a spot at the interesect between the Line defined by Spot Channel 1 and Spot Channel 2 coordinates and an ellipse centered on Spot Channel 1 with Distance Lateral Ref as a semi-minor axis and Distance Axial Ref as a semi-major axis
81	Z Ref (um)	36.825	Float	Coordinate in indicated unit of the reference spot in the along the Z axis. The reference spot is a calculated as a spot at the interesect between the Line defined by Spot Channel 1 and Spot Channel 2 coordinates and an ellipse centered on Spot Channel 1 with Distance Lateral Ref as a semi-minor axis and Distance Axial Ref as a semi-major axis
82	X Ref Shift (um)	0.138	Float	Relative distance in the indicated unit between the reference spot and the spot detected in Channel 1 along the X axis
83	Y Ref Shift (um)	-0.205	Float	Relative distance in the indicated unit between the reference spot and the spot detected in Channel 1 along the Y axis
84	Z Ref Shift (um)	1.654	Float	Relative distance in the indicated unit between the reference spot and the spot detected in Channel 1 along the Z axis
85	Semi Minor Axis (um)	0.343	Float	This is the semi minor axis of an ellipse defining the maximum distance in the XY plan calculated by dividing the largest Practical Lateral Resolution by 2.
86	Semi Major Axis (um)	2.383	Float	This is the semi major axis of an ellipse defining the maximum distance in the Z plan calculated by dividing the largest Practical Axial Resolution by 2.
87	Distance Lateral Ref (um)	0.247	Float	Distance between the reference spot and the spot detected in Channel 1 in the XY plan in the indicated unit
88	Distance Axial Ref (um)	1.654	Float	Distance between the reference spot and the spot detected in Channel 1 in the Z plan in the indicated unit
89	Distance 3D Ref (um)	1.673	Float	Distance between the reference spot and the spot detected in Channel 1 in the 3D in the indicated unit
90	Colocalization Ratio	0.3	Float	Ratio between the 3D Distance (Channel 1 Channel 2) and the 3D Reference Distance. A ratio above 1 indicates the spot detected in channel 2 is further than the pratical resolution of the system. Formula: Colocalization_Ratio = Distance_3D /Distance_3D_Ref

Colocalisation Ratio: A ratio above 1 indicates the distance between the spot detected in channel 1 and 2 is larger than the practical resolution of the system: Channel Alignment is not good and corrections should be performed using the pixel shift table.

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.

Microscopie > 2025/01/22 > QC Scope > image-2025-1-27_20-1-40.png