Event:2011-Registration-Retreat-Tuesday

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 Back to Registration Brainstorming 2011


Tuesday registration topics

  • Grand challenge in registration

Example from the vision community is the face recognition grand challenge


    • Grand challenge, pick problem that current technology fails on. Will have a greater impact that a normal miccai contest, tweaking current methods.
    • Define a general data set, that is complex enough to force new technology.
    • Example data set full body registration, for example mice
    • How do we define good registration,
    • Vanderbilt data set. Blind evaluation and “you cheat you lose” approach.
    • Look at taxonomy, see what checks off: if speed is important, if ...
    • Use a clinical outcome for the quality of the result? Use a secondary system, that relies on the registration to make its decision.
    • Subjective clinical decisions often not reliable (example size of ventricles, normal, enlarged, hugely enlarged)
    • Several grand challenges, for example estimate the uncertainty of the registration.
    • What can today’s method to well? Good start to find a grand challenge.
    • Pig, 1000 lead balls. CT the Pig, move, CT again. Do radiation therapy. Shrink tumors. etc.
    • Need a grant to get such a project going.
    • The balls migrate over time, can we use anatomical landmarks. Can we use features in the data for landmarks that also will be used for driving the algorithm?
    • Error bars on positions of landmarks.
    • Find landmarks, easier in bone, vascularture, gyration patterns, more difficult with breast, and in white matter.
    • Using anatomical feature for registration often robust (vasculature, ..).
    • Define validation strategies that most people agree on, but is strongly related to the applications.
    • What is the aspect, robustness, accuracy, speed? Need to be specific in a challenge.
    • Two types of registration, having visual landmarks, or not. If no visible features, still models of stiffness and physical properties can meaningfully predict movement.
    • Point landmarks, Synthetic data, what are the taxonomy for metrics?
    • Other user (metrics?) critera are: is it to slow, is it useful? Amount of user interaction, etc.
    • Marketing, grant challenge should capture imagination, should not be technology oriented. A vision that can capture attention, and funding.
    • Come up with a medically relevant topic.
    • Asking clinicians if this is good enough, get to the relevance. Then ask the practical questions, is this fast enough, robust enough, ...
  • What works using current technology
  • White paper outline