TubeTK/Events/2010.07.26: Difference between revisions
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= | = Topics = | ||
* | * Dashboards auto update? | ||
* | ** tubetk/CMake/DashboardScripts tubetk/CMake/CTestCustom... | ||
** | * Batch Processing | ||
** | ** Python vs BatchMake vs Any | ||
*** | * Slicer | ||
*** | ** TubeNet Viewer | ||
*** | ** Slicer load .tre | ||
* | ** Slicer Loadable Module | ||
** | * Registration | ||
** | ** Speckle in ultrasound registration | ||
** | ** Model-based deformation field interpolation | ||
*** | ** Fluid deformation (Marc) | ||
* | ** Registration metrics based on ultrasound probe orientation | ||
** | * Segmentation | ||
** | ** Unit test VTree code | ||
*** 3D | |||
*** 2D | |||
** Automated vessel tree extraction | |||
*** Using spatial prior | |||
*** Seed selection | |||
** Automated distinguishing arteries from veins based on spatial prior | |||
* Atlas formation | |||
** Retinal data | |||
*** Email from UIowa (still waiting) | |||
** Brain data | |||
*** Vessel extractions from Liz | |||
** Port pipeline to VTree | |||
= Status = | |||
= Patrick = | == Patrick == | ||
* Primary goal: Bump and dent identification on IC images | * Primary goal: Bump and dent identification on IC images | ||
* Accomplishments | * Accomplishments | ||
** Traveled to SSRL to view the acquisition and meet with Greg and Mike. | |||
** Modified the GenerateFeatures application to handle the input of arbitrary feature images | |||
** Using the previous features and Casey's new patch-based features, was able to achieve 95% pixel level accuracy in Weka and 23/25 defects found with 0 false positives in image space (after morphology). | |||
** Explore new features | ** Explore new features | ||
*** evaluate a variety of standard deviations for intensity and ridge computations | *** evaluate a variety of standard deviations for intensity and ridge computations | ||
* Near Term (Aug 2) | |||
** Receiving code to simulate the tomography directly on GDS Layers | |||
* Near Term (Aug | ** Compute dot-product between line (hessian) tangent and normal directions in ES and GDS images | ||
** | ** Subselect features | ||
** | |||
** Product: ~ 5 slides / report to USC illustrating path chosen, strengths, and weaknesses. | ** Product: ~ 5 slides / report to USC illustrating path chosen, strengths, and weaknesses. | ||
*** Real-world tests / workflow | *** Real-world tests / workflow | ||
Line 39: | Line 48: | ||
**** i.e., do we need to insert modifications for training on every slice / acquisition / ? | **** i.e., do we need to insert modifications for training on every slice / acquisition / ? | ||
**** Normalizing for inter-acquisition (or inter-slice) variations? | **** Normalizing for inter-acquisition (or inter-slice) variations? | ||
** | ** Work with new collaborator at Kitware. | ||
* Medium term ( | * Medium term (August 9) | ||
** Delivery and education | ** Delivery and education | ||
** Can we get better in simulation? | ** Can we get better in simulation? | ||
** chip-to-chip matching | |||
** connectivity analysis | |||
== Casey == | |||
* Primary goal: Compare populations of vascular networks | |||
* Near term (Aug 2) | |||
** Feature extraction | |||
*** Patch-based features (max, median, quantiles) | |||
*** Scales / neighborhood | |||
** Feature research | |||
*** Width estimate | |||
**** Not the same as Gaussian blur | |||
*** New patch features | |||
*** Location of local max in ridgeness | |||
*** Subsample centerlines | |||
*** Optimal match filter | |||
** Classifiers | |||
*** Comparison | |||
*** Implementation for transfer to USC | |||
*** Hierarchy (Good/bad. If bad, then add/sub.) | |||
* Medium term (Aug 9) | |||
** Connectivity analysis | ** Connectivity analysis | ||
** chip-to-chip comparison | |||
= | == Andinet == | ||
* Primary goal: | * Primary goal: Data from Duke for BWH | ||
* | * Accomplishments | ||
** | ** Data from Duke | ||
*** | *** Contacted several folks to gather information ( we had questions regarding the machine at Duke) and check status. | ||
**** | **** BrainLab: Contacted Pratik. He is working on getting us VV license | ||
**** | **** Duke: Contacted Tanya and learned that SD-5000 ultrasound machine is not integrated with the BrainLab system. The machine is used to acquire ultrasound data independently. | ||
*** | **** Aloka: Contacted John Walsh at Aloka and learned that SD-5000 is very old model and doesn't come with research interface | ||
*** | *** Based upon the conversation I had with you, may be we should probably look into saving data off the BrainLab system itself not bother with the SD-5000. | ||
** | ** Write IJ article | ||
*** | *** Made progress writing the IJ article. Hua helped me a lot generating results using synthetic data. We have now a solid outline and some write up in most of the sections | ||
*** | *** You can also access the tex, bib, etc files in my Work directory: Work/Andinet/TensorIJ | ||
* | * Near term (August 2) | ||
** | ** IJ Article | ||
*** | *** Move article to TubeTK/Documentation/2010.TensorIJ | ||
*** Add more texts, figures and results and clean it up more. | |||
** | *** Put together self-contained source code tree containing the classes that we will submit with this paper. | ||
* | *** Refer to TubeTK | ||
** | *** Cite grant proposal in article | ||
** | ** Install VV at Duke | ||
** | * Medium term (August 9) | ||
* | |||
** | |||
* | |||
= Hua = | == Hua == | ||
* Primary goal: ultrasound image processing | * Primary goal: ultrasound image processing | ||
* Accomplished | * Accomplished | ||
** | ** Correct a bug in itkAnisotropicHybridDiffusionImageFilter | ||
*** | ** prepared figures for Andinet's Insight Journal paper. | ||
* Near term (August | ** Improve code coverage for uncovered filters under Application/CLI/** Increase coverage of TubeTK | ||
* Near term (August 2) | |||
** Update registration code | ** Update registration code | ||
** Begin investigation of registration metrics that depend on ultrasound probe orientation | ** Begin investigation of registration metrics that depend on ultrasound probe orientation | ||
*** Get data from InnerOptic | *** Get data from InnerOptic | ||
**** http://www.insight-journal.org/midas/item/view/2206 | **** http://www.insight-journal.org/midas/item/view/2206 | ||
**** http://www.insight-journal.org/midas/item/view/117 | **** http://www.insight-journal.org/midas/item/view/117 | ||
* Medium term ( | * Medium term (August 9) | ||
[[Category:TubeTK Events and Meetings|2010.07.26]] | |||
Latest revision as of 18:45, 26 July 2013
Topics
- Dashboards auto update?
- tubetk/CMake/DashboardScripts tubetk/CMake/CTestCustom...
- Batch Processing
- Python vs BatchMake vs Any
- Slicer
- TubeNet Viewer
- Slicer load .tre
- Slicer Loadable Module
- Registration
- Speckle in ultrasound registration
- Model-based deformation field interpolation
- Fluid deformation (Marc)
- Registration metrics based on ultrasound probe orientation
- Segmentation
- Unit test VTree code
- 3D
- 2D
- Automated vessel tree extraction
- Using spatial prior
- Seed selection
- Automated distinguishing arteries from veins based on spatial prior
- Unit test VTree code
- Atlas formation
- Retinal data
- Email from UIowa (still waiting)
- Brain data
- Vessel extractions from Liz
- Port pipeline to VTree
- Retinal data
Status
Patrick
- Primary goal: Bump and dent identification on IC images
- Accomplishments
- Traveled to SSRL to view the acquisition and meet with Greg and Mike.
- Modified the GenerateFeatures application to handle the input of arbitrary feature images
- Using the previous features and Casey's new patch-based features, was able to achieve 95% pixel level accuracy in Weka and 23/25 defects found with 0 false positives in image space (after morphology).
- Explore new features
- evaluate a variety of standard deviations for intensity and ridge computations
- Near Term (Aug 2)
- Receiving code to simulate the tomography directly on GDS Layers
- Compute dot-product between line (hessian) tangent and normal directions in ES and GDS images
- Subselect features
- Product: ~ 5 slides / report to USC illustrating path chosen, strengths, and weaknesses.
- Real-world tests / workflow
- Does a trained classifier work on other layers?
- Does a trained classifier work on other acquisitions?
- i.e., do we need to insert modifications for training on every slice / acquisition / ?
- Normalizing for inter-acquisition (or inter-slice) variations?
- Work with new collaborator at Kitware.
- Medium term (August 9)
- Delivery and education
- Can we get better in simulation?
- chip-to-chip matching
- connectivity analysis
Casey
- Primary goal: Compare populations of vascular networks
- Near term (Aug 2)
- Feature extraction
- Patch-based features (max, median, quantiles)
- Scales / neighborhood
- Feature research
- Width estimate
- Not the same as Gaussian blur
- New patch features
- Location of local max in ridgeness
- Subsample centerlines
- Optimal match filter
- Width estimate
- Classifiers
- Comparison
- Implementation for transfer to USC
- Hierarchy (Good/bad. If bad, then add/sub.)
- Feature extraction
- Medium term (Aug 9)
- Connectivity analysis
- chip-to-chip comparison
Andinet
- Primary goal: Data from Duke for BWH
- Accomplishments
- Data from Duke
- Contacted several folks to gather information ( we had questions regarding the machine at Duke) and check status.
- BrainLab: Contacted Pratik. He is working on getting us VV license
- Duke: Contacted Tanya and learned that SD-5000 ultrasound machine is not integrated with the BrainLab system. The machine is used to acquire ultrasound data independently.
- Aloka: Contacted John Walsh at Aloka and learned that SD-5000 is very old model and doesn't come with research interface
- Based upon the conversation I had with you, may be we should probably look into saving data off the BrainLab system itself not bother with the SD-5000.
- Contacted several folks to gather information ( we had questions regarding the machine at Duke) and check status.
- Write IJ article
- Made progress writing the IJ article. Hua helped me a lot generating results using synthetic data. We have now a solid outline and some write up in most of the sections
- You can also access the tex, bib, etc files in my Work directory: Work/Andinet/TensorIJ
- Data from Duke
- Near term (August 2)
- IJ Article
- Move article to TubeTK/Documentation/2010.TensorIJ
- Add more texts, figures and results and clean it up more.
- Put together self-contained source code tree containing the classes that we will submit with this paper.
- Refer to TubeTK
- Cite grant proposal in article
- Install VV at Duke
- IJ Article
- Medium term (August 9)
Hua
- Primary goal: ultrasound image processing
- Accomplished
- Correct a bug in itkAnisotropicHybridDiffusionImageFilter
- prepared figures for Andinet's Insight Journal paper.
- Improve code coverage for uncovered filters under Application/CLI/** Increase coverage of TubeTK
- Near term (August 2)
- Update registration code
- Begin investigation of registration metrics that depend on ultrasound probe orientation
- Get data from InnerOptic
- Medium term (August 9)