ITK Release 4/A2D2 Projects/SCORE++/Tcon-2011-04-18: Difference between revisions

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Line 24: Line 24:
** About 100Gb per item
** About 100Gb per item
** Suggestion:
** Suggestion:
*** Per dataset have two tiers: The full dataset, and the small sample to quick evaluation
*** Per dataset have three tiers:  
***# Testing data (data freely available, annotations/truth sequestered)
***# Training data (data and annoations freely available)
***# Small sample for quick evaluation
*** Provide an online viewing tool (MIDAS/DJ Viewer)
* Data Transfer  
* Data Transfer  
** Using MIDAS CPP ?
** Using MIDAS CPP ?
** 100 Gbit /s Transfer rate at Kitware KHQ (about 10 GigaByte/s)
** 100 Gbit /s Transfer rate at Kitware KHQ (about 10 GigaByte/s)
** Use Amazon S3 Store ?
** Use Amazon S3 Store
*** Rates and prices ?
*** Rates and prices prohibitive
* Scoring Algorithms
* Scoring Algorithms
** Client Side
** Client Side and Server Side the same
** Server Side
** Fully open-source
** Provided as
**# ITK classes
**# GenerateCLP Executables for use with BatchMake
**#* How are these distributed?  New toolkit?
** Batchmake scripts provided to show how to run methods and score on a collection of data
**#* How are these distributed?  New toolkit?
* Representations for
* Representations for
** Tracks (3D + t)
** Tracks (3D + t)
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** THEM we must provide a library (IO API) for reading and writing this data.
** THEM we must provide a library (IO API) for reading and writing this data.
** Maybe use the MetaImage library ?
** Maybe use the MetaImage library ?
*** It already have the concepts of "polylines" and "tubes"
*** It already has the concepts of "polylines" and "tubes"


=== Server Side Processing ===
=== Server Side Processing ===


WARNING: THIS IS BRAINSTORMING
WARNING: THIS IS BRAINSTORMING...but we need to do something like this!


* Rationale: The data is too large to download
* Rationale: The data is too large to download

Latest revision as of 23:30, 18 April 2011


Attendees

  • Sean Megason
  • Arnaud Gelas
  • Stephen Aylward
  • Patrick Reynolds
  • Julien Jomier
  • Xiaoxioa Liu
  • Luis Ibanez

Items

  • Data licensing
  • Data Curation
  • Associating publications with the data in the database
  • Comparison of segmentations
  • What MIDAS instance to use ?
  • How much data ?
    • A dozen items..
    • About 100Gb per item
    • Suggestion:
      • Per dataset have three tiers:
        1. Testing data (data freely available, annotations/truth sequestered)
        2. Training data (data and annoations freely available)
        3. Small sample for quick evaluation
      • Provide an online viewing tool (MIDAS/DJ Viewer)
  • Data Transfer
    • Using MIDAS CPP ?
    • 100 Gbit /s Transfer rate at Kitware KHQ (about 10 GigaByte/s)
    • Use Amazon S3 Store
      • Rates and prices prohibitive
  • Scoring Algorithms
    • Client Side and Server Side the same
    • Fully open-source
    • Provided as
      1. ITK classes
      2. GenerateCLP Executables for use with BatchMake
        • How are these distributed? New toolkit?
    • Batchmake scripts provided to show how to run methods and score on a collection of data
        • How are these distributed? New toolkit?
  • Representations for
    • Tracks (3D + t)
    • Object Ids
    • Use ITK SpatialObjects ?
      • 3D + time ?
    • Use itk::LabelMaps to represent segmentations and tracks

Tracking Evaluation

  • ICCV paper: Tracking performance evaluation
  • Sometimes the data start from meshes
    • Sometimes it starts from images
    • Sometimes it starts from tracks
  • If adopting a file format for storing this data
    • THEM we must provide a library (IO API) for reading and writing this data.
    • Maybe use the MetaImage library ?
      • It already has the concepts of "polylines" and "tubes"

Server Side Processing

WARNING: THIS IS BRAINSTORMING...but we need to do something like this!

  • Rationale: The data is too large to download
    • Users could upload
      • Source code
      • Binaries
      • Virtual Machines
    • These code/binaries will be executed on the data and the output will go to the evaluation.

Action Items

  • File formats for Tracks
    • Luis, Arnaud, DJ
  • Track Evaluation Metrics (comparing tracks ICCV)
    • Marcel
  • Segmentation Metrics
    • Patrick, Xiaoxiao
  • Web Data Visualization
    • Patrick, DJ, Arnaud, Luis
  • IO / Database
    • Arnaud, Luis, DJ, Zack Mullen
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