Image Quality in Digital Pathology

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Image Quality in Digital Pathology

• (from a pathologists 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 Years 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.)

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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
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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 HE?
• 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 doesnt feel like weve 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 -
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Images, image quality and observer performance
new horizons in radiology lecture. Kundel HL.
Radiology. 1979 Aug132(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

• Doesnt 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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270 µm pixel pitch of monitor
Magnification at the monitor
1 pixel 270 µm at the monitor 1 pixel 10 µm
at the sensor 270 / 10 27X
27X magnification from sensor to monitor
1 pixel 10 µm at the sensor 1 pixel 0.25 µm
at the sample 10/0.25 40X
40X magnification from object to sensor
1080X TOTAL magnification from object to
monitor This is the equivalent of a 108X
objective on a microscope!!??
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Near point 10
Scan Type Magnification Magnification Magnification Effective Viewing Magnification (at 11) Effective Viewing Magnification (at 11) Effective Viewing Magnification (at 11) Manual Scope Equivalent Objective Magnification Manual Scope Equivalent Objective Magnification Manual Scope Equivalent Objective Magnification
Scan Type Object to Sensor Sensor to Monitor TOTAL 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
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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?

• hoj_at_upmc.edu