Difference between revisions of "Event:2011-Registration-Retreat-Tuesday"
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| − | + | * 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== | |
Revision as of 17:02, 22 February 2011
Home < Event:2011-Registration-Retreat-TuesdayBack to Registration Brainstorming 2011
Contents
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, ...
