TubeTK/Events/2010.07.12: Difference between revisions
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(Created page with ' = Andinet = * Primary goal: Data from Duke for BWH * Near term (2 weeks, July 2nd) ** Attend NAMIC AHM ** Determine what is necessary to record data sent to OpenIGTLink from Ve…') |
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= Andinet = | = Andinet = | ||
* Primary goal: Data from Duke for BWH | * Primary goal: Data from Duke for BWH | ||
* | * Accomplishments | ||
** Attend NAMIC AHM | ** Attend NAMIC AHM | ||
** Determine what is necessary to record data sent to OpenIGTLink from VectorVision system | ** Determine what is necessary to record data sent to OpenIGTLink from VectorVision system | ||
| Line 8: | Line 7: | ||
*** Begin implementation | *** Begin implementation | ||
*** Product: powerpoint presentation: 5 slides | *** Product: powerpoint presentation: 5 slides | ||
* Medium term (1.5 months, August | * Near term (August 1) | ||
** Install VV at Duke | |||
** Determine if we can get US data from Duke machine | |||
** Write IJ article | |||
*** Cite grant proposal in article | |||
* Medium term (1.5 months, August 15) | |||
** Investigate simulation of ultrasound from MR/CT | ** Investigate simulation of ultrasound from MR/CT | ||
** | ** Code review of vessel segmentation method from Stephen | ||
= Patrick = | = Patrick = | ||
* Primary goal: Bump and dent identification on IC images | * Primary goal: Bump and dent identification on IC images | ||
* | * Accomplishments | ||
** Explore new features | ** Explore new features | ||
*** z-score values from three different mean/stdDev joint histograms: add, subtract, and unchanged | *** z-score values from three different mean/stdDev joint histograms: add, subtract, and unchanged | ||
*** evaluate a variety of standard deviations for intensity and ridge computations | *** evaluate a variety of standard deviations for intensity and ridge computations | ||
*** compute dot-product between line (hessian) tangent and normal directions in ES and GDS images | ** New centerline method (skeletonization) | ||
*** Product: ~ 5 slides to USC illustrating path chosen, strengths, and weaknesses. | ** GenerateFeaturesForWeka | ||
* Medium term (1 | * Near Term (Aug 1) | ||
** | ** compute dot-product between line (hessian) tangent and normal directions in ES and GDS images | ||
** write program that goes from weiki output to image and computes TPR/FPR scores on that image | |||
** Collaborate with Casey | |||
*** Choose classification scheme | |||
*** Implement in C++ or python - in tubetk | |||
**** Use neuralnets / parzenWindowing in ITK | |||
*** Subselect features | |||
*** Large number of test cases | |||
** Product: ~ 5 slides to USC illustrating path chosen, strengths, and weaknesses. | |||
** Report to USC involving TPR/FPR for new technique | |||
** Go to Synchrotron | |||
* Medium term (1 months, August 15) | |||
** Delivery and education | |||
** Real-world tests / workflow | |||
*** Does a trained classifier work on other layers? | |||
*** Does a trained classifier work on other chips? | |||
**** i.e., do we need to insert modifications for training on every slice / acquisition / ? | |||
**** Normalizing for inter-acquisition (or inter-slice) variations? | |||
** Connectivity analysis | |||
** Can we get better in simulation? | |||
** Get registered data from Greg | |||
= Casey = | = Casey = | ||
Revision as of 17:43, 12 July 2010
Andinet
- Primary goal: Data from Duke for BWH
- Accomplishments
- Attend NAMIC AHM
- Determine what is necessary to record data sent to OpenIGTLink from VectorVision system
- Define data workflow and software architecture
- Begin implementation
- Product: powerpoint presentation: 5 slides
- Near term (August 1)
- Install VV at Duke
- Determine if we can get US data from Duke machine
- Write IJ article
- Cite grant proposal in article
- Medium term (1.5 months, August 15)
- Investigate simulation of ultrasound from MR/CT
- Code review of vessel segmentation method from Stephen
Patrick
- Primary goal: Bump and dent identification on IC images
- Accomplishments
- Explore new features
- z-score values from three different mean/stdDev joint histograms: add, subtract, and unchanged
- evaluate a variety of standard deviations for intensity and ridge computations
- New centerline method (skeletonization)
- GenerateFeaturesForWeka
- Explore new features
- Near Term (Aug 1)
- compute dot-product between line (hessian) tangent and normal directions in ES and GDS images
- write program that goes from weiki output to image and computes TPR/FPR scores on that image
- Collaborate with Casey
- Choose classification scheme
- Implement in C++ or python - in tubetk
- Use neuralnets / parzenWindowing in ITK
- Subselect features
- Large number of test cases
- Product: ~ 5 slides to USC illustrating path chosen, strengths, and weaknesses.
- Report to USC involving TPR/FPR for new technique
- Go to Synchrotron
- Medium term (1 months, August 15)
- Delivery and education
- Real-world tests / workflow
- Does a trained classifier work on other layers?
- Does a trained classifier work on other chips?
- i.e., do we need to insert modifications for training on every slice / acquisition / ?
- Normalizing for inter-acquisition (or inter-slice) variations?
- Connectivity analysis
- Can we get better in simulation?
- Get registered data from Greg
Casey
- Primary goal: Compare populations of vascular networks
- Near term (2 weeks, July 2nd)
- Review previous processing pipeline with Stephen
- Research on methods for comparing spatial graphs / adjacency matrices
- Begin Port and test existing adjacency code
- Medium term (1.5 months)
- Process retinal data
- Complete port and test of existing adjacency code
- Prepare IJ article
Hua
- Primary goal: ultrasound image processing
- Near term (2 weeks, July 2nd)
- Verify Andinet's code: add tests and help with IJ publication
- Creating tests (sin pattern with known derivatives)
- Done by next wednesday (June 23)
- Choose Michel vs BWH
- Verify Andinet's code: add tests and help with IJ publication
- Medium term (1.5 months
- Investigate use of speckle in ultrasound registration
- Model-based deformation field interpolation
- 2D-3D registration (data)
- Simulating ultrasound from MR/CT
