arvydas laurinavi č ius pathology visions 2010
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The Results of Automated Image Analysis Workshop at the 10th European Congress on Telepathology and 4th International Congress on Virtual Microscopy. Arvydas Laurinavi č ius Pathology Visions 2010. VILNIUS UNIVERSITY. NATIONAL CENTRE OF PATHOLOGY. Background and Disclaimer. - PowerPoint PPT PresentationTRANSCRIPT
The Results of Automated Image Analysis Workshop at the
10th European Congress on Telepathology and 4th International Congress on Virtual Microscopy
Arvydas Laurinavičius
Pathology Visions 2010
VILNIUS UNIVERSITY NATIONAL CENTRE
OF PATHOLOGY
Background and Disclaimer
• Pathologist (Renal)• Director, National Center of Pathology, LT• Professor, Vilnius University• EU COST Telepathology Network• EU LLL EUROPALS• MB, IHTSDO (SNOMED CT)• International Member, CAP• User of Aperio and TissueGnostics tools• No competing interests
2010 Vilnius Lithuania2012 Venice Italy
Telepathology - Programhttp://www.telepathology2010.com/31Screen clipping taken: 2010-09-27; 11:14
http://www.telepathology2010.com
The Goal
To provide an overview of automated image analysis tools in terms of their robustness and workflow efficiency in a structured and comparable fashion
Outline
• Why – A Pathologist’s Vision of the Digital
• How – Workshop Design and Results
• What – Ways to Go
>19th 20th 21st century
Evolution of Pathology
DIGITALMOLECULAR
IMUNOHISTOCHEMISTRYMICROSCOPYMACROSCOPY
Spectrum of Technologies
Pathology Lab …
transforms biological information into medical
Biospecimen
Pathology Diagnosis
Patient
Clinical Decision
Spectrum of Technologies
Adding Digital Path-WayTissue collected
Tissue sampled
Tissue processed
Slides produced
Pathologist ”reads” slides
Pathologist interprets images Computer
scans slides
Computer analyzes images
Digital patholo
gy
Competition? Ignorance? Synergy?
MacroscopyMicroscopy
Immunohistochemistry
Questions asked:
• Does this work?• Why is Digital better than Conventional?• Tool or Toy? Long way to go…
More specifically:• Shall I scan everything?• Should scanners be certified for diagnostic use?• Is it legal to make a diagnosis on virtual slides?• Can I work faster on digital images?• Are quantification results reliable?
Innovation versus Routine
Psychology
Technology
Involvement needs awareness
Treat the Tools and Humans equally
Pathologist #1 Pathologist #1
Pathologist #2
perfect
Pathologist #2moderate
Tool #1 Tool #1
Tool #2
perfect
Tool #2perfect
inte
r-ob
serv
er
intra-observer
mod
erate
???
Are different tools in agreement? Are they better than we?
Partitioning the Observer
Tissue collected Tissue sampled Tissue
processed
Slides produced
Pathologist ”reads” slides
Pathologist interprets images
Computer scans slides
Computer analyzes images
1st European Scanner Contest
Automated Image Analysis Workshop
“2 in 1”
“2 in 1”
“Software” “Hardware”
Outline
• Why – A Pathologist’s Vision of the Digital
• How – Workshop Design and Results
• What – Ways to Go Next
Workshop Design
• Keep simple, explore feasibility of a Contest • Estrogen Receptor and HER2 IHC
– Whole slide and TMAs from the Spanish QA Program
• HER2 FISH– Whole slides from the Ntl Ctr Pathol
• Available for scanning >1 month (at the 1st ESC)• Participants presented their workflow and results
at the Workshop• Presentations posted at http://www.telepathology2010.com
Participants
Company Speaker IHC FISH
Aperio Kate Lillard √
Leica/SlidePath Sean Costello √
BioImagene Vikram Mohan √ √
MetaSystems Christian Schunck √*
3DHistech Csaba Hankó √ √
* Used for analysis the FISH slides provided
Workshop Results
Concordance testing of the results was out
of scope, however, some output data
provided by the Participants were
analyzed
Estrogen Receptor, % Pos Nuclei3 outlier cases by A, variable ROI selection?
Estrogen Receptor, Total Nuclei CountedB and C, different size of ROI?
Participant B and C Correlation 0.898 p<.0001
Estrogen Receptor, % Pos NucleiStrong correlation; nonlinearity possible?
Nonlinear regression p<.0001
B
Estrogen Receptor, % Pos NucleiNonlinearity: C outputs higher values (frequent 100%) than B
Estrogen Receptor, % Pos NucleiB tends to output lower values than A and C
Not significant
HER2 IHC Score Agreement between B and C
C 0 1+ 2+ 3+ n/a TotalB0 4 0 0 1 0 5
1+ 4 4 1 0 0 9
2+ 0 0 1 0 0 1
3+ 0 2 2 16 0 20
n/a 1 0 0 0 4 5
Total 9 6 4 17 4 40
Simple Kappa 0.61Weighted Kappa 0.69 Note: different cutoff used by B and C for 3+ (10 vs 30%)
Lessons learned
• Plan thoroughly, involve Participants
• Improve scanning logistics, especially FISH
• Provide gold standard slides, preferably TMAs
• Define sampling
– whole slide, manual annotation, automated ROI
detection
• Harmonize output formats
Outline
• Why – A Pathologist’s Vision of the Digital
• How – Workshop Design and Results
• What – Ways to Go
Ways to Go
• Do nothing
• Do the same
• Do inter-observer (inter-Tool) variability study
• Develop an ongoing QA program
• Disseminate the results
Disseminate
D-PathLympics
Digital Pathology League
Scanner Contest
Image Analysis Contest