Event:2011-Registration-Retreat-Tuesday

 Back to Registration Brainstorming 2011

Tuesday registration topics

1 Grand challenge in registration

The motivation for having a grand challenge in registration is to define a problem where current technology fails, and that is interesting for the community to work on. This will likely lead to novel and relevant solutions. A grand challenge will likely have larger impact than more traditional short term contests that aims at finding the best method available with current technology and careful settings of algorithmic parameters. The task will be to register data sets that are complex enough to force new technology to be developed. Example of grand challenges from other communities, such as for example the vision community is the DARPA Grand Challenges and the Face Recognition Grand Challenge

• 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, ...