Understanding Digital Imaging and Digital Pathology Systems: Today s Capabilities and Tomorrow s Potential

Transcription

1 Understanding Digital Imaging and Digital Pathology Systems: Today s Capabilities and Tomorrow s Potential Ulysses J. Balis, M.D. Director, Division of Pathology Informatics Department of Pathology University of Michigan Health System ulysses@med.umich.edu 35 OPS TFLOPS PFLOPS 1

2 Larger Context of the Path Leading to the Digital Pathology Department: not a question of if but when The emergence of patient safety and service/operational gains made possible by the collective technologies that comprise Digital Pathology are significant. While not all the changes are immediately imminent, there is little question that the transformations pathology will experience will be sooner than many consider, and similarly, more substantive This process should not be feared but rather, embraced Enjoy the Vogon Poetry --Douglas Adams from Hitchhiker s Guide to the Galaxy Overview A brief history of digital pathology Where we are: 2009 Anticipated potential: near and long term Classical SWOT analysis Likely adoption cycle The need for standards Some Comments on CBIR Open discussion and questions 2

3 A Brief History of Telepathology and Digital Pathology 1960 s: Experimentation with a dedicated video feed between Logan International Airport and MGH in Boston 1970 s and 1980 s: Experimentation with static images / store and forward Late 1970 s: Mayo rents space on a commercial TV satellite 1986: Ron Weinstein proposes the use of robotics microscopy for remote consultation 1990: First appearance of robotics-based solutions: Corrabi, Apollo 1993: First whole slide scanners start to appear (tile based) 1999: Second Generation whole slide scanners under development Much faster Line scan and push broom acquisition technologies 2002: First appearance of commercial Where we are: 2009 Approaching commoditization The real value propositions are shifting from equipment to: Service models Software Use of the imagery data (CBIR etc) And ultimately, greater support of the vision of personalized medicine Those practices that explore the service opportunities right now will be much better positioned to take advantage of new technologies, software and service models that emerge (i.e there is no longer a compelling reason to delay deployment of those use s that make sense) 3

4 Anticipated Potential: Near and Long term Near Term Niche areas Specific value-added propositions Partnership models Long Term All-digital shops Tight regional integration with fostering of subspecialty expertise being immediately available in real time Possible empowerment of the solo practitioner in outreach areas (not necessarily a threat to local practices) Advanced decision support, search and quantitative tools Transparency in proficiency testing and competency assessment Strengths Weaknesses Opportunities Threats SWOT 4

5 Adoption Cycle Niche Areas Quantitative interpretation Advanced Preview of Externally provisioned special studies (ipox, etc.) Integration of digital data into multi-modality interpretive data sets PET/CT/Histology Fusion data sets Morphogenomics Morphoprotenomics Ultimately, fusion with clinical phenotype and trajectory Standards Image Based DICOM IHE Metadata Based HL7 2.x 3.x (RIM) Convergence Based LITS-Interop 5

6 Whole slide images 12 gigapixels (for one image) each can be 2-20 images 1Gb even after image compression A different challenge than faced Radiology 20 years ago X ray image from Dah Current World View of Pathology Imagery Repositories Model 1: Relational Database Image Metadata associated with -level data Entire Schema required to carry out discovery Text-based Image data is a passive component of the query Model 2: Metadata-tagged Images Image Metadata associated with each image Image becomes a self-contained dataset available for discovery Text-based Image data is a passive component of the query Entry in master accession table Associated and image descriptors Associated and Associated Associated image descriptors and and image image descriptors descriptors 6

7 Thesis Statement Extraction from Image repositories based upon spatial information The availability of digital whole slide data sets represent an enormous opportunity to carry out new forms of numerical and datadriven query, in modes not based on textual, ontological or lexical matching. Search image repositories with whole images or image regions of interest Carry our search in real-time via use of scalable computational architectures or Analysis of data in the digital domain Resultant Surface Map or gallery of matching images The Challenge That Is Pathology CBIR Start with some conservative initial assumptions, concerning a prototypic image repository, in terms of search potential: Ability to search 10 years of data 1000 slides day 200,000 slides/year 500 Mb of compressed whole slide data/slide Operational goal of being able to: Search in real-time Re-index the database every evening, such that searches carried out the next day are current 7

8 The Challenge That Is Pathology CBIR Net storage required for ten year s worth of data: 1 Billion Megabytes 10 6 Gigabytes 10 3 Terabytes 10 0 Petabytes 1 Petabyte Current conservative enterprise storage is $2000/ Terabyte The full Petabyte would cost $2M A single Genetic-type search across all images, assuming 5 seconds of computation / slide, would be: 200,000 slides x 10 x 5 seconds 5 million seconds This is 6 log too slow 8.27 weeks or about 6 searches per year» (original Apple 2e: 78 years) So we would need to save our queries for those really important image searches. Conventional VQ, which is ~100 times faster, is still not fast enough: 13.8 hours per feature search Yet another 4 log of performance is required Two ways to address this:» 10,000 parallel processors or» better algorithms On Current Technology Modern computational throughput continues to increase, with this capability representing an opportunity for perhaps 1-2 log performance increase in the next decade With a one-log increase, we are still left with a five-log gap that needs to be made up by improved algorithmic performance. 8

9 Recent Developments A number of promising algorithms being developed Support Vector Machines (SVM) Principle Component analysis High-dimensional reduction approaches Spatially-invariant VQ (SiVQ) The Dark Side of the Moon for Digital Pathology What are the areas of Digital Pathology where we likely will be unprepared? Inadequacy of contemporary interoperability standards Storage expenses Provenance of data Longitudinal stewardship of primary digital data Management of the culture of change is far more complex than the information technology itself We must be superb managers and informaticians, as well as laboratorians, to steward the coming change to a successful outcome Inaction is not an option 9

10 Closing Remarks Digital pathology has arrived, functional performance is adequate. Those who explore this field now are less likely to fall behind Those who explore this field now will gain a competitive advantage 10