image quality in digital pathology
DESCRIPTION
Image Quality in Digital Pathology. (from a pathologist’s perspective) Jonhan Ho, MD, MS. Disclosure. Image Quality: define/measure. Image quality is good enough if:. It has a resolution of 0.12345 μ /pixel It is captured in XYZ color space/pixel depth - PowerPoint PPT PresentationTRANSCRIPT
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Image Quality in Digital Pathology
(from a pathologist’s perspective)Jonhan Ho, MD, MS
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Disclosure
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Image Quality: define/measure
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Image quality is good enough if:
• It has a resolution of 0.12345 μ/pixel• It is captured in XYZ color space/pixel depth• It has a MTF curve that looks perfect• It has a focus quality score of 123• Has a high/wide dynamic range
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What is “resolution”?
• Spatial resolution• Sampling period• Optical resolution• Sensor resolution• Monitor resolution• New Year’s resolution???????
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Optical resolution
• Theoretical maximum resolution of a 0.75 NA lens is 0.41μ. 1.30 NA – 0.23μ.
• Has NOTHING to do with magnification! (we will get to that later.)
NA
R61.0
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Depth of Field
• As aperture widens– Resolution improves– Depth of field narrows• Less tissue will be in focus
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Image quality is good enough if:
• It has a resolution of 0.12345 μ/pixel• It is captured in XYZ color space/pixel depth• It has a MTF curve that looks perfect• It has a focus quality score of 123• Has a high/wide dynamic range
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Image quality is good enough if it is:
• “Sharp”• “Clear”• “Crisp”• “True”• “Easy on the eyes”
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Image quality is good enough if it is:
• “Sharp”• “Clear”• “True”
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Image quality is good enough if:
• You can see everything you can see on a glass slide
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Image quality is good enough if:
• I can make a diagnosis from it
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Image quality is good enough if:
• I can make as good a diagnosis from it as I can glass slides.– This is a concordance study
• OK, but how do you measure this?!?!?!?!?!
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Gold standard = Another Diagnosis
Glass
Observer
Original?
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Concordance validation
• Some intra-observer variability• Even more interobserver variability• Order effect• “great case” effect
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Concordance validation
• Case selection– Random, from all benches?– Enriched, with difficult cases?– Presented with only initial H&E?• Allow ordering of levels, IHC, special stains?• If so, how can you compare with the original diagnosis?
– Presented with all previously ordered stains?• If so, what about diagnosis bias?
– How old of a case to allow?
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Concordance validation
• Subject selection– Subspecialists? Generalists?– Do all observers read all cases, even if they are not
accustomed to reading those types of cases?– Multi-institutional study• Do observers read cases from other institutions?• Staining/cutting protocol bias
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Concordance validation
• Measuring concordance– Force pathologist to report in discrete data
elements?• This is not natural! (especially in inflammatory
processes!)• What happens if 1 data element is minimally
discordant?
– Allow pathologist to report as they normally do?• Free text – who decides if they are concordant? How
much discordance to allow? What are the criteria?
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Concordance study bottom line
• Very difficult to do with lots of noise• Will probably conclude that can make equivalent
diagnoses• At the end, we will have identified cases that are
discordant, but what does that mean?– What caused the discordances?
• Bad images? If so what made them bad?• Familiarity with digital?• Lack of coffee?!?!?!
• Still doesn’t feel like we’ve done our due diligence – what exactly are the differences between glass and digital?
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PERCEPTION = REALITY
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PERCEPTION = QUALITY
“Sharp, clear, true”
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Psychophysics
• The study of the relationship between the physical attributes of the stimulus and the psychological response of the observer
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What we need is -
Image
Image quality
Observer Performance
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Images, image quality and observer performance: new horizons in radiology lecture. Kundel HL. Radiology. 1979 Aug;132(2):265-71
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Kundel on image quality
• “The highest quality image is one that enables the observer to most accurately report diagnostically relevant structures and features.”
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Receiver Operator Curve (ROC)
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Conspicuity index formula
• K = f(Size, contrast, Edge Gradient/surround complexity)
• Probability of detection = f(K)
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Kundel, 1979
• “Just as a limited alphabet generates an astonishing variety of words, an equally limited number of features may generate an equally astonishing number of pictures.”
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Can this apply to pathology?
• What is our alphabet? MORPHOLOGY!– Red blood cells– Identify inflammation by features
• Eosinophils• Plasma cells
– Hyperchromasia, pleomorphism, NC ratio– Build features into microstructures and macrostructures– Put features and structures into clinical context and
compare to normal context– Formulate an opinion
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Advantages of feature based evaluation
• Better alleviates experience bias, context bias• Can better perform interobserver concordancy• Connects pathologist based tasks with
measurable output understandable by engineers
• Precedent in image interpretability (NIIRS)
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NIIRS 1
“Distinguish between major land use classes (agricultural, commercial, residential)”
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NIIRS 5
“Identify Christmas tree plantations”
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Disadvantages of feature based evaluation
• Doesn’t eliminate the “representative ROI” problem
• Still a difficult study to do– How to select features? How many?– How to determine gold standard?– What about features that are difficult to discretely
characterize? (“hyperchromasia”, “pleomorphism”)
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Bottom line for validation
• All of these methods must be explored as they each have their advantages and disadvantages– Technical– Diagnostic concordance– Feature vocabulary comparison
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Image perception - Magnification
• Ratio• Microscope– Lens– Oculars
• Scanner– Lens– Sensor resolution– Monitor resolution– Monitor distance
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40X magnification from object to
sensor
1 pixel = 10 µm at the sensor1 pixel = 0.25 µm at the sample
10/0.25 = 40X
270 µm pixel pitch of monitor
~27X magnification from sensor to
monitor
1 pixel =270 µm at the monitor 1 pixel = 10 µm at the sensor
270 / 10 = ~27X
= 1080X TOTAL magnification from object to monitor
This is the equivalent of a 108X objective on a microscope!!??
Magnification at the monitor
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Scan Type
Magnification Effective Viewing Magnification (at 1:1)
Manual Scope Equivalent Objective Magnification
Object to
Sensor
Sensor to
MonitorTOTAL 10” 24” 48” 10” 24” 48”
20X 20 27 540 540 225 112.5 ~54x ~22.5x ~11.3x
40X 40 27 1080 1080 450 225 ~108x ~45x ~22.5x
Near point = 10”
What if the sensor was obscenely high resolution?
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Other things that cause bad images
• Tissue detection• Focus
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Tissue detection
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What about Phantoms?
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One final exercise in image perception
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