Tissue Image Analysis 2.0
Training Presentation
Control #: 13MAN1231.A1
Effective Date: 12-Jul-12
ECO #: 3472
2012
Tissue IA 2.0 Training Presentation
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Intended Use
IMPORTANT:
SlidePath applications are not cleared by the FDA, Health Canada, or in the EU for
diagnostic or clinical use. All applications are intended solely for use in the research
or educational setting, such as university or pharmaceutical development. These
applications are described as Research Applications or Research Use Only.
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What is Tissue Image Analysis
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High-throughput web enabled image analysis solution for digital slides
Control and review image analysis online anywhere using web browser
Analyze whole slides, areas of interest or TMAs
Queue multiple images for batch processing
Automatically integrate data with existing TMA/Research data in Distiller and/or
export to Excel spreadsheets
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Introduction to Tissue Image Analysis
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OverView of Tissue IA
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Set preferences in Digital Image Hub
Determine the color definition file
What is positively stained
Use pre defined deconvolution colour definition files
Determine the algorithm preferences
Select the preferences for the biomarker of interest
Test the image analysis in a variety of fields and slides
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OverView of TIA
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Run image analysis in Digital Image Hub
Run on whole slides, TMAs (with appropriate licence) annotations
Results are added as slide metadata
Run high throughput image analysis in Distiller (with an applicable licence)
Run on TMAs
Results are automatically entered into records
Each user will only have access to the preferences and color definition files they
created
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Optimizer Harness
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Optimizing Harness
Use the Harness to optimize the color definition files and algorithm setting before
sending to high throughput analysis
Color
Definition file
Algorithm
settings
Preference
File
High through
put analysis
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Image Analysis Harness Optimizer
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In Digital Image Hub, select a slide
In the slide viewer, zoom to 20x magnification and select 'Image Analysis Harness
Optimizer'
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How To Set
Color Definition Files
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Color Definition File
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Determine what is positively stained
Select 'Manage Color Definition files'
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Color Definition Files
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A list of color definition files will appear
Create, rename, copy, edit or delete files
Preview results of color definition files
Note: List of preferences is user specific, i.e. each user will only have access to their
own preferences
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PreLoaded Color Definition Files
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Default DAB
Deconvolution processing allows for a
More advanced method of color separation
Preloaded Deconvolution Files:
Deconvolution – Haematoxylin
Deconvolution – AEC
Deconvolution – DAB
Deconvolution – Eosin
Deconvolution – Fast Blue
Deconvolution – Fast Red
Deconvolution – Methyl Green
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Create a Color Definition File
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Select pixels to include in the color definition file
Green = pixels already included
Select other colors to indicate positive pixels
Use up / down arrow to increase / decrease number of pixels included
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Inverted Screen
Use the 'T' key to toggle to the inverted screen
Select pixels to be subtracted from the color definition file
Red = pixels not included
color definition mask
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Inverted Screen mask
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Test Color Definition Files
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Objective: record the full representation of positive color, regardless of location
Test the color definition file in a variety of areas across a number of slides
Test on areas of high and low intensity staining
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Demonstration: Color Definition Files
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How To Set
Algorithm Preferences
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Select the Algorithms
There are two standard immunohistochemical algorithms
Measure Stained Area
Measure Stained Cells
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Select Algorithm Preference
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A list of preferences will be displayed
Cannot create new preferences from scratch, must copy an existing default
preference
Test, rename, edit and delete
Note: List of preferences is user specific, i.e. each user will only have access to their
own preferences
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Running the Algorithm on a Field of View or Area
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Field of View or Area
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Run the algorithm on the field of view (area which is visible in the viewer at 20x)
Run algorithm on a selected area by selecting the square
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Creating Preference File for
Measure Stained Area Algorithm
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Select the Color Definition File
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Up to three classes/ color definition
files can be defined
Analysis will be carried out on all
three classes simultaneously
Choose either a pre loaded
color definition file or one that
has been created/edited by the user
Use lock to freeze the preferences warning
this action cannot be undone!
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Input Parameters
Goal:
Adjust the input parameters to optimize the Algorithm for the biomarker of
interest
Changing the input parameters will affect the over all results
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Input Parameters
Measurement Units
Define the measurement units
0 = µm, 1= mm, 2 = pixels
Calibration setting
Set default calibration setting
Range between 0-10
Use if there is no magnification information
attached to the image (i.e. Jpeg images )
Deconvolution setting
Select color deconvolution
Enable = 1, select if deconvoluted color have been selected
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Tissue Threshold Intensity
Determine what is background
and what is tissue
Background Intensity Setting
Select a value between 0 (black)
and 255 (white)
Typically set between 210 – 240
Example of original tissue
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Threshold set to 50
Threshold set to 210
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Threshold set to 250
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Deconvolution Threshold Setting
Determine the intensity threshold for
each of the color Classes defined
Maximum intensity of a pixel in the
Class to be considered positive
Select a value between 0 (black)
and 255 (white)
Example of original tissue
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Example of threshold setting 50
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Example of threshold setting 200
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Measure Stained Area Results
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Run the positive pixel algorithm on a field
of view or area
Back to refine preferences
Reanalyse
Store results will create an annotation with
the results of the image analysis
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Measure Stained Area Results
Measurement units
As defined during input settings
Total tissue area
Total area of the tissue which has been
identified
Positive Area of Class A/B/C
Total area which is positive for each of
the classes defined
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Measure Stained Area Results
Average staining intensity class A/B/C
The modal value of a greyscale intensity histogram of the positively stained
pixels
Used to calculate Staining Concentration
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Staining concentration Class A/B/C
Measure of the concentration of the stain within the tissue
Co-localization of Classes
The area of overlap between classes
Pearsons correlation coefficient
The correlation between two classes
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Tissue masks
Tissue mask represents the deconvoluted image for each class defined, threshold cut
off for each class defined and total mask
Example of masks images where two color classes were applied. Class A: DeconvolutionHaematoxylin, Class B: Deconvolution -DAB
Original tissue
Deconvoluted DAB mask
Tissue threshold mask
Deconvoluted Haematoxylin mask Haematoxylin intensity threshold
DAB intensity threshold
Class
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Total mask: Haematoxylin : Red, DAB: Green,
AB: Yellow
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Tissue Masks
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Tissue masks apply primary colours to each class
Class A: Red
Class B: Green
Class C: Blue
The co-localization of these channels is a mix of both colours
Class AB: Yellow
Class BC: Magenta
Class AC: Cyan
Class ABC: Grey
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Demonstration: Algorithm Preferences
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Measure Stained Cells Algorithm
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Measure Stained Cells
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Measure stained cells replacing the Nuclear, membrane and intercellular algorithms
With the use of new input parameters including Nuclear Heterogeneity detection, TIA
2.0 has more advanced nuclear detection
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Measure Stained Cells
Measure stained cells replacing the Nuclear, membrane and intercellular algorithms
Analyse nuclear, cytoplasm or membrane staining separately or a combination of any
two
Up to three of the
following (as long as two
of them use the same
color definition file)
nuclear, cytoplasm or
membrane marker
Set input parameters for
each of the biomarkers
Nuclear Counterstain
Results output:
Separate results for
each of the biomarkers
selected.
For two biomarkers are
defined, combination
and co-localization
results will be output
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Color Definition File
Select a color definition file for
nuclear counterstain
AND
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Of the following:
nuclear marker, cytoplasmic marker,
membrane marker
If all cellular compartments are selected,
ensure that two of the compartments have
the same colour definition file assigned
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Input Parameters
Measurement units
Define the measurement units
0 = µm, 1= mm, 2 = pixels
Tissue Threshold
Determine what is background and what
is tissue
Default calibration
Set default calibration setting
Range between 0-10
(use if there is no magnification provided
i.e. Jpeg images)
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Nuclei Heterogeneity
How similar the nuclei are in the tissue
Range between 0 – 4, 0 = nuclei are similar, >1 = increasing diversity in the tissue
from darkest to lightest
0
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Increased diversity from darkest to lightest
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Nuclei Heterogeneity
Original tissue
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Nuclei Heterogeneity = 1
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Nuclei Heterogeneity = 4
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Strength of the Nuclear Counter Stain
Define if the counter stain is strong or weak
Range between 0-2, 0= strong, 2 = weak
High Contrast
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Low intensity
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Strength of the Nuclear Counter Stain
Use 0 for high Contrast:
Use 2 for low contrast:
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Nuclear Window
Define the size of the window around the nucleus by setting its radius
Values in units defined in measurement input , i.e. if measurement units are set to
pixels, then this parameter is in pixels
Affects nuclei per window and nuclear area per window settings
SlidePath recommends this setting be left at 37 µm to start
radius
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Nuclear Area
Exclusion parameter
Range between 0 -10,000 units squared as defined in measurement units
50µm2
150µm2
50µm2
150µm2
Area:: 100µm2
Area: 300µm2
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Nuclei of interest included
The slider is set to 50µm and 150µm, nuclei of interest included
as its area is 100µm2
Nuclei of interest excluded
The slider is set to 50µm and 150µm, nuclei of interest excluded
as its area is 300µm2
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Nuclear Area
Nuclear area set between 0- 1000
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Nuclear area set between 0- 10
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Nuclei per window
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Exclusion parameter
A “window” = as defined in the input setting
Range: 0 – 1000 cells per window
Cell will be counted if its centre point lies within the window
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Nuclei Per window
5
20
Nuclei of interest excluded
The slider is set to 5 and there are only 3 surround nuclei in the
window (green)
5
20
Nuclei of interest included
The slider is set to 20 and there are 10 surround nuclei in the
window (green)
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Nuclei Per window
Nuclei per window set between 0-100
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Nuclei per window set between 60-100
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Nuclear Area Per Window
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Exclusion parameter
Based on percentage of area within the window that is taken up by nuclei
Range: 0 – 100%
Nucleus is counted if its centre point lies within the window
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Nuclear Area Per Window
Nuclei of interest excluded
Nuclei of interested excluded, as area taken up by surrounding nuclei
is 10%
Nuclei of interest excluded
Nuclei of interested excluded, as area taken up by surrounding nuclei
is 80%
Nuclei of interest included
Nuclei of interested included, as area taken up by surrounding nuclei
is 70%
Nuclei of interest included
Nuclei of interested included, as area taken up by surrounding nuclei
is 70%
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Nuclear Area per Window
% Nuclear per window set between 0-100%
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% Nuclear per window set between 10-50%
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Cell Area
Eliminate cells on the basis of size
Range between 0 – 10,000 (measurements units as defined during input parameters)
50µm2 150µm2
Cell of interest included
The slider is set to 50µm and 150µm, cell of interest
included as its area is 100µm2
Area: 100µm2
50µm2
Area::
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300µm2
150µm2
Cell of interest excluded
The slider is set to 50µm and 150µm, cell of interest
excluded as its area is 300µm2
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Cell Area
Example of original tissue
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Threshold set from 0 -500
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Threshold set between 0-50
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Maximum Cell Radius
The maximum cell radius which should be included for analysis
Range between 0 – 1000 (measurement as defined in ‘Measurement units’ input
parameters)
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Cell radius mainly influences how the cells are modeled (i.e. how much the cell
boundary expands during the cell prediction)
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Nuclear Staining Intensity Cutoff
All pixels with intensity higher than defined in deconvoluted nuclear staining image
are considered negative
Range 0-255
0
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255
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Nuclear Staining Intensity Cutoff
Nuclear staining intensity cut off set at 210
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Nuclear staining intensity cut off set at 150
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% of Stained Pixels in a Nucleus Cutoff setting
Categorization parameter
Range: 0 – 100%
Nuclei with a lower percentage of positive staining will be categorized as negative
0
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100%
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% of Stained Pixels in a Nucleus Cutoff setting
Nuclear stained area cut off set 80%
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Nuclear stained area cut off set 10%
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Strong/Moderate/Weak Nuclear Staining Intensity Cutoff
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Categorization parameter
Range: 0 – 255
0 represents minimum intensity (black), 255 represents maximum intensity (white),
in a greyscale intensity range
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Strong/Moderate/Weak Nuclear Staining Intensity Cutoff
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Strong/Moderate Staining Intensity Cutoff Setting
Nuclei with a level below this input are categorized as strong
Moderate/Weak Staining Intensity Cutoff Setting
Nuclei with a value above this input are categorized as weak
Nuclei with values between are categorized as moderate
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Strong/Moderate/Weak Nuclear Staining Intensity Cutoff
Staining intensity low threshold
set to 96, high threshold set to
196
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Staining intensity low threshold set
to 163, high threshold set to 196
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Nuclear Staining Filters
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Define if all, only positive, or only negative, nuclear staining should be included in
results and output image
Parameter works on cells not pixels
Range 0-2, 0 representing all nuclei, 1 representing only positive nuclei, 2
representing only negative nuclei
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Nuclear Staining Filter
Include all cells and nuclei
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Include only positive cells
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Only include negative cells
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Cytoplasmic Input Parameters
Cytoplasmic parameters will only have an effect on Cytoplasmic input parameters if
cytoplasmic stain is selected
Cytoplasmic input parameters
Cytoplasmic Staining Intensity Cut-off
% of Cytoplasmic Staining Area in a Cell Cut-off
Strong/Moderate/Weak Cytoplasmic Staining Intensity Cut-off
Cytoplasmic staining filter
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Cytoplasmic Staining Intensity Cutoff
All pixels with intensity higher than defined in deconvoluted cytoplasmic staining
image are considered negative
Range 0-255
0
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255
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Cytoplasmic Staining Intensity Cutoff
Original image
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Cytoplasmic stained intensity cut off
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Cytoplasmic stained intensity cut off set
220
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% of Cytoplasmic Staining Area in a Cell Cut-off
Categorization parameter
Range: 0 – 100%
% of Stained Pixels in cytoplasm Cutoff setting
Cytoplasm with a lower percentage of positive staining will be categorized as
negative
0
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100%
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% of Cytoplasmic Staining Area in a Cell Cut-off
Original tissue
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Cytoplasmic stained area cut off
set to 75%
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Cytoplasmic stained area cut off
set to 95%
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Strong/Moderate/Weak Cytoplasm Staining Intensity
Cutoff
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Strong/Moderate Staining Intensity Cutoff Setting
Cytoplasm with a level below this input are categorized as strong
Moderate/Weak Staining Intensity Cutoff Setting
Cytoplasm with a value above this input are categorized as weak
Cytoplasm with values between are categorized as moderate
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Strong/Moderate/Weak Cytoplasm Staining Intensity
Cutoff
Original tissue
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Strong/Moderate/Weak
cytoplasm staining intensity
cutoff set between 100-150
(From 0-100: strong, 100-150:
moderate, 150-255: weak)
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Strong/Moderate/Weak
cytoplasm staining intensity
cutoff set between 100-150
(From 0-160: strong, 160-220:
moderate, 220-255: weak)
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Cytoplasm Staining Filters
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Define if all, only positive, or only negative, Cytoplasm staining should be included in
results and output image
Range 0-2, 0 representing all cells, 1 representing only positive cells, 2 representing
only negative cells
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Membrane Input Parameters
Membrane parameters will only have an effect on membrane input parameters if
cytoplasmic stain is selected
Membrane input parameters
Membrane Staining Intensity Cut-off
Percentage of Membrane Staining Area in a Cell Cut-off
Strong/Moderate/Weak Membrane Staining Intensity Cut-off
Membrane staining filter
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Membrane Staining Intensity Cutoff
All pixels with intensity higher than defined in deconvoluted membrane staining
image are considered positive
Range 0-255
0
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255
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Membrane Staining Intensity Cutoff
Original Tissue
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Membrane staining intensity
cutoff set to 75
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Membrane staining intensity
cutoff set to 220
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% of Stained Pixels in a Membrane Cutoff setting
Categorization parameter
Range: 0 – 100%
% of Stained Pixels in a membrane Cutoff setting
Membrane with a lower percentage of positive staining will be categorized as
negative
0
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100%
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% of Stained Pixels in a membrane Cutoff setting
Original tissue
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Membrane staining intensity cuttoff
set to 75%
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Membrane staining intensity cuttoff
set to 95%
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Strong/Moderate/Weak Membrane Staining Intensity
Cutoff
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Strong/Moderate Staining Intensity Cutoff Setting
Membrane with a level below this input are categorized as strong
Moderate/Weak Staining Intensity Cutoff Setting
Membrane with a value above this input are categorized as weak
Membrane with values between are categorized as moderate
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Strong/Moderate/Weak Membrane Staining Intensity
Cutoff
Original tissue
Strong/Moderate/Weak membrane staining
intensity cutoff set between 100-150
(From 0-100: strong, 100-150: moderate,
150-255: weak)
Strong/Moderate/Weak
membrane staining intensity cutoff set
cutoff set between 160-220
(From 0-160: strong, 160-220:
moderate, 220-255: weak)
Strong Membrane: Violet, Moderate membrane: Pink, Weak Membrane: Rose
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Membrane Staining Filters
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Define if all, only positive, or only negative, Membrane staining should be included in
results and output image
Range 0-2, 0 representing all nuclei, 1 representing only positive cells, 2 representing
only negative cells
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Membrane Filters
All membrane include (setting= 0)
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Only included positive membrane
setting = 1
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Only included negative membrane
setting = 2
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Results
Measurement units
As defined during input settings
Total tissue area
Total area of the tissue which has been
identified
Total number of cells
The total number of cells that have been
identified in the tissue
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Results
Histoscore
Histoscore of the accepted nuclei
Total Accepted Nuclei
All nuclei that were not eliminated by
exclusion criteria
Number and percentage of negative, weak,
moderate, strong intensity nuclei
The total number nuclei Categorized as
having negative, weak, moderate, strong
intensity by value and percentage
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Results
Average nuclear staining intensity
The modal value of a greyscale intensity histogram of the positively stained
pixels
Used to calculate Staining Concentration
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Average nuclear Staining absorbance
Measure of the absorbance of the stain within the tissue
Percentage nuclear area in Tissue
The percentage of nuclear area
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Cytoplasmic Marker Results
Cellular H-Score for cytoplasmic staining
Number and % of cells with negative cytoplasmic staining
Number and % of cells with positive cytoplasmic staining
Number and % of weak, moderate and strong intensity stained cytoplasm cells
Average cytoplasmic staining intensity – of positive area only
Average cytoplasmic staining absorbance - of positive area only
% positive cytoplasmic area in tissue
Note: Only visible if cytoplasmic marker is selected
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Membrane Marker Results
Cellular H-Score for membrane staining - Number and % of cells with negative
membrane staining
Number and % of cells with positive membrane staining
Number and % of weak, moderate and strong intensity stained membrane
cells
Average membrane staining intensity – of positive area only
Average membrane staining absorbance – of positive area only
% positive membrane area in tissue
Note: Only visible if membrane marker is selected
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Measure Stained Cells Mask
Original tissue
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Deconvoluted nuclear counterstain
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Deconvoluted nuclear marker
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Deconvoluted membrane marker
Detected Membrane
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Nuclear marker below set cutoff
Accepted nuclei and cell borders
Membrane staining
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Membrane marker below set cutoff
Co-localized Nuclear and
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Testing Preference
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Once the algorithm preferences have been set, test on a variety of areas across a
number of slides
Test on areas with high and low intensity staining
Once satisfied, lock the color definition file and the settings in the algorithm
preferences
The algorithm can now be run in high throughput
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Demonstration: Measure Stained Cells
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Running Preference Files in
High Throughput Analysis
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Image Analysis in Digital Image Hub
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Use Digital Image Hub to run image analysis on whole slides, TMAs and annotations
Create custom jobs with exclusion regions, and merge results
From the browse tab, select the images to be analyzed
Send images to Tissue IA workflow
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Select Algorithm and Preferences
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Select the algorithm to run, the desired preferences
Select the areas to be analyzed
Whole slide
Annotations
OpTMA cores (on provision of appropriate licence)
Custom job
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Select Algorithm and Preferences
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Select merged results
Select analysis magnification
Enter job name and description
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Analysis Magnification
Warning!
Tissue IA has been designed to run at 20x magnification
Positive pixel algorithm can be run at lower magnifications
If the slide has been scanned at 40x magnification, please ensure that the slide is run
at 20x
Note: very small annotations may not run
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Custom Job
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Select areas for analysis
Exclude regions from analysis
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Tissue IA Tab
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After submitting a job to IA, you are brought to the Tissue IA tab
The Tissue IA tab consists of 5 subtabs
Dashboard
Completed jobs
Queued jobs
Failed jobs
Custom jobs
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Dashboard
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Overview of IA and job status
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Completed Jobs Tab
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View list of completed jobs
View results, download data, re-run, view folder, delete job
Note: Job cannot be re-run if an image from the job has been deleted
View the preferences and color definition file used
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Results
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Image Analysis Results are stored as metadata
View the results of the image analysis by:
Info icon under completed jobs
Info icon under browse tab
Mousing over the annotations (most recent run results displayed)
Click on the Excel icon to export the Image Analysis results into Excel
On completed jobs page
Under slide information
View IA job history under slide information
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Queued jobs
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Jobs submitted to Image Analysis are queued
View queued jobs
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Failed Jobs Tab
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Any jobs that failed are listed
Can re-queue or re-create the IA job
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Custom Jobs Tab
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All custom jobs in progress are listed here
Work on jobs and then submit
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Sending Annotations for High Throughput Analysis
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Tissue
Tissue
IA IA
2.02.0
Training
Training
Presentation
Presentation
© SlidePath 2012
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High Throughput Analysis on Annotations
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Following drawing an annotation, right click over the annotations and select ‘analyse
annotation’
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High Throughput Analysis on Annotations
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Select the algorithm, algorithm preference and magnification which the algorithm
should be run at
Job Id will be returned for easy tracking of the job under completed jobs tab
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Demonstration:
High Throughput Analysis
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Important Information
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Multifocal Images
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For any image scanned in multiple planesimage analysis will be run on the middle
plane
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Freehand Annotations
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When drawing freehand annotations for analysis, please ensure that the annotation is
sealed using the “seal annotation” tool
Note: The annotation will not be included in analysis if not sealed
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Out of Focus Images
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Images that have been scanned and are out of focus will affect the quality of Image
Analysis results
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Magnification and Annotations
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High throughput Image Analysis in Digital Image Hub can be run at different
magnifications
Warning: If annotations are small and run at 4x or 10x, they may fail to process. As a
general rule, analysis should be run at a magnification equal or greater than the
magnification the annotation was drawn at, to ensure that the analysis will complete
successfully
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Optional Algorithms
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Creating Preference for Measure Stained Area
Fluorescence Algorithm (Optional)
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Measure Stained Area Fluorescence
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Analyses the number of positive pixels in a fluorescence images
This algorithm is only applicable to supported fluorescence image formats in .scn
This algorithm can be run on any magnification.
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Select color Channel
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Select up to three color channels
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Input Parameters
Intensity Threshold Channel A/B/C
Adjust depending on the number of channels selected
Range between 0-255 (0=Black, 255= White)
Set the minimum and maximum intensity
thresholds for each of the input channels
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Define the measurement units
0 = µm, 1= mm, 2 = pixels
Set default calibration setting
Range between 0-10
Use when images don't have calibration information
(i.e Jpeg)
Use lock to freeze the preferences warning this action
cannot be Undone!
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Results
Measurement units
As defined during input settings
Positive Area of Channel A/B/C
Total area which is positive for each of
the channel defined
Average staining intensity Channel A/B/B
The modal value of a greyscale intensity
histogram of the positively stained pixels
Used to calculate Staining Concentration
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Co-localization of Channel
The area of overlap between classes
Pearsons correlation coefficient
The correlation between two channels
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Measure Stained Area Fluorescence Masks
Original Image
Fluorescence images are changed to a greyscale image
Each channel represented as greyscale image
Image Channel Cutoff
Image threshold for each channel using the defined input parameters
Note: Accepted pixels are denoted by a white mask; black pixels are concerned
background and are not included in the analysis
Total Mask
Color mask representation of each separated channel and the over lapping between
each channel
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Measure Stained Area Fluorescence Masks
Example of Fluorescence analysis; Channel A = Green spectrum, Channel B = DAPI,
Channel C = Red spectrum
Original Tissue
Channel B greyscale image
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Channel A greyscale image
Chanel B threshold image
Channel C greyscale image
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Chanel A threshold
Chanel C threshold image
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Tissue Masks
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Tissue masks apply primary colours to each class
Channel A: Red
Channel B: Green
Channel C: Blue
The co-localization of these channels is a mix of both colours
Channel AB: Yellow
Channel BC: Magenta
Channel AC: Cyan
Channel ABC: Grey
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Demonstration: Measure Stained Area
Fluorescence
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Her2 for research purpose only Algorithm
(optional)
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HER2 Algorithm Annotations
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Algorithm does not have contextual understanding of tissue
Invasive regions must be annotated for analysis
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HER2 Algorithm Input
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Algorithm preferences and color definition file are not editable
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Her2
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Predicted HER2 score
% Confidence in 0/1, 2+ and 3+ score
Membrane Staining Absorbance
% Membrane Positive Pixels
% Continuously Stained Membrane
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Her2 Results
Predicted HER2 score
% Confidence in 0/1, 2+ and 3+ score
Membrane Staining Absorbance
% Membrane Positive Pixels
% Continuously Stained Membrane
Unprocessed image of breast tissue that has been
immunohistochemically stained with antibodies probing for
HER-2 protein expression
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Areas detected as positive for continuous membrane staining by
image analysis are highlighted in green
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Demonstration Her2
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Creating Preference File for
Microvessel Detection Algorithm (Optional)
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Color File
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Select the correct color definition file from the drop down menu
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Tissue Threshold
Determine what is background
and what is tissue
Background Intensity Setting
Select a value between 0 (black)
and 255 (white)
Typically set between 210 – 240
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Max Vessel Intensity
Set maximum vessel intensity
This input allows segmentation of vessels from surrounding tissue
Select a value between 0 (black) and 255 (white)
Maximum vessel intensity = 190
Maximum vessel intensity = 150
Yellow (0-1.5); orange (1.5-2), Blue: >2
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Set Minimum Vessel Size
Eliminate small vessels on the basis of size
Range from 0-50,000 pixels
Vessels with an area less than the input value will not be included in analysis
Minimum vessel size = 20
Minimum vessel size = 300
Yellow (0-1.5); orange (1.5-2), Blue: >2
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Set Maximum Vessel Size
Eliminate large vessels on the basis of size
Settings range from 0 – 50,000 pixels
Vessels with an area greater than the input value will not be included in analysis
Maximum vessel size = 2000
Maximum vessel size = 4000
Yellow (0-1.5); orange (1.5-2), Blue: >2
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Set Maximum Vessel Aspect Ratio
Eliminate vessels on the basis of aspect ratio
Settings range from 0 – 3000
Input parameter represents max(length/width)*100
Maximum vessel aspect ratio = 400
Maximum vessel aspect ratio = 1000
Yellow (0-1.5); orange (1.5-2), Blue: >2
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Separate Merged Vessels
0 = Separate merged vessels
1 = Do not separate
Separate merged vessels = 0
Separate merged vessels = 1
Yellow (0-1.5); orange (1.5-2), Blue: >2
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Merge Close Vessels
Set strength of merging for close vessel segments
Setting range 0-10
This parameter connects close components and smoothens vessel edges
Higher value = stronger merging of vessels
Merge close vessels = 1
Merge close vessels = 10
Yellow (0-1.5); orange (1.5-2), Blue: >2
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Microvessel Detection – Results
Total Number of Vessels
Total number of identified vessels,
with or without lumen
Total Tissue Area in Pixels
Total number of pixels in the tissue
Total Vessel Area with Lumen In Pixels:
Total number of pixels identified as
vessel in the image, including regions
identified as lumen
Average Vessel Area In Pixels:
Average number of pixels per vessel in the image,
excluding regions identified as lumen
Average Vessel Area with Lumen In Pixels:
Average number of pixels per vessel in the image,
including regions identified as lumen
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Microvessel Detection – Results
Average Vessel Perimeter In Pixels:
Average number of pixels forming perimeter per vessel
Note: This output is not affected by the presence of vessel lumen
Microvessel Density – Number of Vessels per Tissue Pixel:
Number of vessels per tissue pixel, including regions identified as lumen
Microvessel Density – Number of Vessels Without Lumen per Tissue Pixel:
Number of vessels per tissue pixel, excluding regions identified as lumen
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Tissue Masking
Original Tissue
Stained tissue (All tissue that is
Tissue Threshold
stained according to the color definition
file)
All vessels not eliminated by size or aspect ratio
Ratio : Yellow (0-1.5); orange (1.5-2)
Blue: >2
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All vessels. Lumen in red (all identified vessels not
eliminated on the basis of size or aspect ratio)
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Demonstration: Microvessel Detection
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Optional Modules
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Integration Toolkit
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The Integration Toolkit allows the user to upload customized algorithm, which have
been developed for other image process programs
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www.leica-microsystems.com/products/digital-pathology
customersupport@slidepath.com
+353 (0)1 8667830
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