The document discusses the advancements in digital microscopy and pathology, highlighting the innovations by Pathomation, a company co-founded by the authors. It details the author's background in computer sciences and bioinformatics, as well as the technological aspects of digital microscopy, including whole slide imaging and data management. The presentation concludes that digital pathology is poised for significant impact in education, research, and clinical workflows, emphasizing the importance of proper implementation and the potential of algorithms in enhancing pathology practice.

1. The advent of
digital
microscopy
Yves Sucaet,
Wim Waelput,
Peter In’t Veld
IT / ComS edition 

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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.

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Topics for today
• How did I get here?
• Digital microscopy/pathology
• How does it work (technology)
• Big images, Big data, and deep learning

5. 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

8. 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

21. 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
Percentofcontentserved
91.86% of the tiles were
served below 200 ms,
including network time.

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And does it scale?
Numberoftilesservedwithin10minutetimeframe
10 minute intervals

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Facilitating the European Society of Pathology

29. 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

44. 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

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Continue the conversation
• Email: yves.sucaet@gmail.com
Thank you for inviting me!