Event:2011-Registration-Retreat-Tuesday

 Back to Registration Brainstorming 2011

Tuesday registration topics

1 Grand challenge in registration

Example from the vision community is the DARPA Grand Challenges and 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, ...