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 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

What is “resolution”? Spatial resolution Sampling period Optical resolution Sensor resolution Monitor resolution New Year’s resolution???????

Optical resolution Theoretical maximum resolution of a 0.75 NA lens is 0.41μ NA – 0.23μ. Has NOTHING to do with magnification! (we will get to that later.)

Depth of Field As aperture widens – Resolution improves – Depth of field narrows Less tissue will be in focus

Image quality is good enough if: It has a resolution of 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

Image quality is good enough if it is: “Sharp” “Clear” “Crisp” “True” “Easy on the eyes”

Image quality is good enough if it is: “Sharp” “Clear” “True”

Image quality is good enough if: You can see everything you can see on a glass slide

Image quality is good enough if: I can make a diagnosis from it

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?!?!?!?!?!

Gold standard = Another Diagnosis Glass Observer Original? Digital Observer Other?

Concordance validation Some intra-observer variability Even more interobserver variability Order effect “great case” effect

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?

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

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?

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?

PERCEPTION = REALITY

PERCEPTION = QUALITY “Sharp, clear, true”

Psychophysics The study of the relationship between the physical attributes of the stimulus and the psychological response of the observer

What we need is - Image Image quality Observer Performance

Images, image quality and observer performance: new horizons in radiology lecture. Kundel HL. Radiology Aug;132(2):265-71

Kundel on image quality “The highest quality image is one that enables the observer to most accurately report diagnostically relevant structures and features.”

Receiver Operator Curve (ROC)

Conspicuity index formula K = f(Size, contrast, Edge Gradient/surround complexity) Probability of detection = f(K)

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.”

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

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)

NIIRS 1 “Distinguish between major land use classes (agricultural, commercial, residential)”

NIIRS 5 “Identify Christmas tree plantations”

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”)

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

Image perception - Magnification Ratio Microscope – Lens – Oculars Scanner – Lens – Sensor resolution – Monitor resolution – Monitor distance

40X magnification from object to sensor 1 pixel = 10 µm at the sensor 1 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

Scan Type Magnification Effective Viewing Magnification (at 1:1) Manual Scope Equivalent Objective Magnification Object to Sensor Sensor to Monito r TOTAL 10”24”48”10”24”48” 20X ~54x~22.5x~11.3x 40X ~108x~45x~22.5x Near point = 10” What if the sensor was obscenely high resolution?

Other things that cause bad images Tissue detection Focus

Tissue detection

What about Phantoms?

One final exercise in image perception

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