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Information Access to Medical Image
  Data: from Big Data to Semantics -
 Academic and Commercial Challenges
Adrien Depeursinge
Henning Müller
Overview
 •    Motivation & objectives
 •    eHealth research at the HES-SO in Sierre
 •    VISCERAL
      •    ETHZ vs. HES-SO
 •    Khresmoi
      •    HES-SO vs. ATOS, Ontotext, (ELDA, HON, GAW)
 •    Conclusions


                                                          2
Motivation for image management
 •  “An image is worth a thousand words”

 •  Medical imaging is estimated 
    to occupy 30% of world 
    storage capacity in 2010!
 •  Mammography data in the 
    US in 2009 amounts to 
    2.5 Petabytes



                              Riding the wave – how Europe can gain from
                              the rising tide of scientific data, report of the
                              European Commission, 10/2010.
                                                                                 3
Objectives of our work
 •    Better exploit visual information in medical
      imaging for decision support
      •    Find similar cases, use these including outcomes for
            diagnosis support
 •    Develop scalable solutions that allow treating
      the volumes produced in hospitals
      •    Detect small regions of interest in medical images
      •    Map images to semantics, store only regions of interest
 •    Link information in reports with image data
      •    Make work of radiologists more efficient
                                                                      4
eHealth at the HES-SO in Sierre
 •    Many eHealth activities since 2007
      •    eHealth unit since 2010
      •    20 persons and three professors
      •    Michael Schumacher, Henning Müller
 •    Several types of projects
      •    EU FP7 projects (Khresmoi, PROMISE, WIDTH,
            VISCERAL, MD-Paedigree, Commodity12, …)
      •    FNS projects (MANY, NinaPro, …)
      •    CTI, Hasler, COST, HES-SO, NanoTerra, mandates

                                                             5
Projet loop in MedGIFT




                          6
Big data challenges and opportunities
 •    Signal data in the images needs to be mapped to
       semantic information
       •    Reduce amount of data to be kept accessible
       •    Get information for decision support
       •    Regions of interest can be extremely small

 •    Simple and efficient tools are required
       •    And these might work better on big data (and need to be
            scalable)

 •    Many rare diseases could be analyzed
       •    These are difficult as people do not know them, they are
            missed and incorrectly treated
       •    Use all data instead of small scale studies
       •    Use data across hospitals, quality is important
                                                                      7
VISCERAL
•    EU funded project (2012-2015)
     •    HES-SO, ETHZ, UHD, MUW, TUW, Gencat
     •    Coordination action, so not research in itself
•    Organize competitions on medical 
     image analysis on big data (10-40 TB)
     •    All computation done in the cloud, 
          collaboration with Microsoft
     •    Identifying landmarks in the body
     •    Finding similar cases
•    Annotation by medical doctors
                                                            8
Objectives of VISCERAL
 •    Create a cloud-based infrastructure to test
      algorithms on big and potentially confidential
      data
 •    Annotate large amounts of medical image data
      for system evaluation (annotate once, reuse)
      •    Annotation in Hungary to keep costs limited
      •    3D annotation and labels in the RadLex terminology
 •    Support the coordination of research work on
       relevant objectives in medical imaging
      •    Including academic groups and companies such as
            Microsoft, Siemens, Toshiba, etc.
                                                                 9
Evaluations in VISCERAL
Test




                           10
KHRESMOI
 •  4 year, 10’000’000 € budget




                                   11
Khresmoi goals
•    Trustable information adapted to each user group
     •    All tools as open source
•    Extract semantic information from all sources
     •    LinkedLifeData




                                                      12
Current status
 •    Project at the beginning of year 3 of 4 years
      •    Half-time
 •    User tests have started among the three user
      groups (much feedback on prototypes expected)
      •    Different types of interfaces
      •    Eye tracking
 •    Implement changes
      to adapt to the user
      groups

                                                       13
Software architecture




                         14
User interfaces




                   15
User tests




              16
Public/private  academic collaborations
•    Close collaboration between actors is beneficial
     •    Different view points on the same problems
     •    Different ways of being evaluated (publications, projects, $)
     •    For larger projects the best partners are necessary
•    Interdisciplinary work is enriching
     •    Creates new ideas (and sometimes frustrations)
     •    Is needed in most fields of computer science
•    Innovation is often the goal of funding
     •    HES has developers, PhD students and senior researcher
          collaborating on the same problems
                                                                      17
Eye tracking
•  http://www.youtube.com/watch?v=YWo1Cx3jdOo




                                             18
Demo
 •  www.youtube.com/watch?v=cMoONC0Tz2c




                                           19
Questions?
 •    More information can be found at 
      •    http://medgift.hevs.ch/
      •    http://publications.hevs.ch/
      •    http://khresmoi.eu/
      •    http://visceral.eu/


 •    Contact:
      •    Henning.mueller@hevs.ch


                                           20

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Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges

  • 1. Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges Adrien Depeursinge Henning Müller
  • 2. Overview •  Motivation & objectives •  eHealth research at the HES-SO in Sierre •  VISCERAL •  ETHZ vs. HES-SO •  Khresmoi •  HES-SO vs. ATOS, Ontotext, (ELDA, HON, GAW) •  Conclusions 2
  • 3. Motivation for image management •  “An image is worth a thousand words” •  Medical imaging is estimated to occupy 30% of world storage capacity in 2010! •  Mammography data in the US in 2009 amounts to 2.5 Petabytes Riding the wave – how Europe can gain from the rising tide of scientific data, report of the European Commission, 10/2010. 3
  • 4. Objectives of our work •  Better exploit visual information in medical imaging for decision support •  Find similar cases, use these including outcomes for diagnosis support •  Develop scalable solutions that allow treating the volumes produced in hospitals •  Detect small regions of interest in medical images •  Map images to semantics, store only regions of interest •  Link information in reports with image data •  Make work of radiologists more efficient 4
  • 5. eHealth at the HES-SO in Sierre •  Many eHealth activities since 2007 •  eHealth unit since 2010 •  20 persons and three professors •  Michael Schumacher, Henning Müller •  Several types of projects •  EU FP7 projects (Khresmoi, PROMISE, WIDTH, VISCERAL, MD-Paedigree, Commodity12, …) •  FNS projects (MANY, NinaPro, …) •  CTI, Hasler, COST, HES-SO, NanoTerra, mandates 5
  • 6. Projet loop in MedGIFT 6
  • 7. Big data challenges and opportunities •  Signal data in the images needs to be mapped to semantic information •  Reduce amount of data to be kept accessible •  Get information for decision support •  Regions of interest can be extremely small •  Simple and efficient tools are required •  And these might work better on big data (and need to be scalable) •  Many rare diseases could be analyzed •  These are difficult as people do not know them, they are missed and incorrectly treated •  Use all data instead of small scale studies •  Use data across hospitals, quality is important 7
  • 8. VISCERAL •  EU funded project (2012-2015) •  HES-SO, ETHZ, UHD, MUW, TUW, Gencat •  Coordination action, so not research in itself •  Organize competitions on medical image analysis on big data (10-40 TB) •  All computation done in the cloud, collaboration with Microsoft •  Identifying landmarks in the body •  Finding similar cases •  Annotation by medical doctors 8
  • 9. Objectives of VISCERAL •  Create a cloud-based infrastructure to test algorithms on big and potentially confidential data •  Annotate large amounts of medical image data for system evaluation (annotate once, reuse) •  Annotation in Hungary to keep costs limited •  3D annotation and labels in the RadLex terminology •  Support the coordination of research work on relevant objectives in medical imaging •  Including academic groups and companies such as Microsoft, Siemens, Toshiba, etc. 9
  • 11. KHRESMOI •  4 year, 10’000’000 € budget 11
  • 12. Khresmoi goals •  Trustable information adapted to each user group •  All tools as open source •  Extract semantic information from all sources •  LinkedLifeData 12
  • 13. Current status •  Project at the beginning of year 3 of 4 years •  Half-time •  User tests have started among the three user groups (much feedback on prototypes expected) •  Different types of interfaces •  Eye tracking •  Implement changes to adapt to the user groups 13
  • 17. Public/private academic collaborations •  Close collaboration between actors is beneficial •  Different view points on the same problems •  Different ways of being evaluated (publications, projects, $) •  For larger projects the best partners are necessary •  Interdisciplinary work is enriching •  Creates new ideas (and sometimes frustrations) •  Is needed in most fields of computer science •  Innovation is often the goal of funding •  HES has developers, PhD students and senior researcher collaborating on the same problems 17
  • 20. Questions? •  More information can be found at •  http://medgift.hevs.ch/ •  http://publications.hevs.ch/ •  http://khresmoi.eu/ •  http://visceral.eu/ •  Contact: •  Henning.mueller@hevs.ch 20