the troy lectures: the advent of digital microscopy (it/coms edition)
TRANSCRIPT
The advent of digital
microscopy
Yves Sucaet, Wim Waelput,
Peter In’t Veld
IT / ComS edition
02-05-2023 pag. 2
Financial disclosure
• Yves Sucaet and Wim Waelput are co-founders and shareholders in Pathomation, an innovative company founded in 2012. The company strives to offer the most comprehensive software platform for digital pathology possible. The focus is on integration, scalability, and user-friendliness. Pathomation implements digital pathology in a variety of use cases and scenarios.
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Prelude
• In October 2016, I was honored at Troy University as one of its 2016 “alumni of the year” during the annual homecoming activities.
• In the following week, I gave several guest lectures in various departments across campus.
• This is the lecture as presented for the Information Systems (IT/ComS) department on Thursday, October 20, 2016.
02-05-2023 pag. 4
Topics for today
• How did I get here?• Digital microscopy/pathology• How does it work (technology)• Big images, Big data, and deep learning
PERSONAL BACKGROUND
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Who am I (education)?
• 1998-2000: Hogeschool Gent (BE)– BS Computer Sciences
• 2001-2005: Troy State University (US)– Exchange program
• Developed an interest in using ComS to help (molecular) biologists
– MS Biological Sciences• Research in yeast genetics with Dr. Christi Magrath (NSF fellowship)
• 2005-2010: Iowa State University– PhD Bioinformatics & Computational Biology
Education
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Who am I (professional)
Professional
• 2000-2001: Becton Dickinson• 2010-2013: HistoGeneX
• Section head Data Management & Bioinformatics
• 2012-now: Pathomation• Chief Technology Officer
• 2014-Q1 2017: VUB• Digital Pathology Manager
• 2016-now: HistoGeneX• Data scientist
WHAT IS DIGITAL MICROSCOPY/PATHOLOGY?
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This is not a digital microscope
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Getting started with digital microscopy
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Whole slide imaging (single slide)
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Hardware
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Software stack
Very large
image!
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How big are these images?
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Network topology at the VUB
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Confusing your end-users (customers)! NOT good!
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What’s the solution?
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Digital microscopy at the VUB
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What does the Pathomation software look like?
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Digital (r)evolution
HOW DOES IT WORK?
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How do the whole slide scanners work?
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Rendering HUGE image files (gigapixel)
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How much data are you transferring?
• It depends• The original file is about 1GB– But you only transfer data in packages of 512x512 px– Optimize the speed of transfer by toggling the
compression ratio• No impact on diagnostic accuracy!
– Tiles are downloaded in parallel• Browser initiates 6 parallel downloads
– Tiles 7, 8, 9… are queued• Optimize tile size for screen size
– Mobile devices vs. 4K screens
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So… how much data ARE you transferring?
• We wrote a profiler application
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Time taken to serve tiles
Time is milliseconds
Perc
ent o
f con
tent
serv
ed 91.86% of the tiles were served below 200 ms,
including network time.
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And does it scale?N
umbe
r of ti
les s
erve
d w
ithin
10
min
ute
timef
ram
e
10 minute intervals
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Facilitating the European Society of Pathology
BIG IMAGES, BIG DATA, AND DEEP LEARNING
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Once you have image data…
• Commercially available • Free-of-charge, open source
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Pancreas analysis for diabetes research
Step 1: find tissue
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Pancreas analysis for diabetes research
Step 1: find tissue
Step 2: locate the islets
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Pancreas analysis for diabetes research
Step 1: find tissue
Step 2: locate the islets
Step 3: quantitate insulin
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More advanced: graph theory (Ackermann)
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More advanced: graph theory (Ackermann)
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More advanced: graph theory (Ackermann)
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More advanced: graph theory (Ackermann)
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But what does the graph mean (Ackermann)?
• Measured degree distributions show that:– Cell positions are not random in the tissue– CD30+ cells cluster in the tissue– the cell graphs are not scale-free
• NextGen Sequencing, proteomics, microarrays etc…– Are NOT the “answer to everything”– Tissue is STILL the issue• Topology matters!
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Deep learning as the new frontier (Van der Laak)
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Another way of looking at deep learning (Van der Laak)
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Do this for histology (Van der Laak)
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Does it work (Van der Laak)?
Pathologist Exb METURadboudumcHMS & MIT
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Computational pathology as a decision support tool
Tumour
IN CLOSING
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Conclusions
• Digital pathology is ready for prime time– Education and training, – Research (including biobanking)
• DIY digital pathology– Do your due diligence: hardware, software• But even more important: ALGORITHMS
– DON’T spend all your resources on “stuff”• Hire the right people to implement the right workflows
– All levels of IT expertise are needed!
– Start with one use case, expand to others– Image analysis can significantly enhance the
profession of pathologists (wide open field!)
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Learn more about digital pathology
