Summer ITKv4 ClinicalGroupMeetingNotes: Difference between revisions

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== 3D Real-Time Physics-Based Non-Rigid Registration for Image Guided Neurosurgery
== Overlap and Similarity ==
== Overlap and Similarity ==
* Level of integration questions.
* Level of integration questions.
* Bridges to 3rd party libs
* Distribution questions: How to build/package with 3rd party libs. How to distribute data.
* Distribution questions: How to build/package with 3rd party libs. How to distribute data.


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* Registration framework
* Registration framework


 
== 3D Real-Time Physics-Based Non-Rigid Registration for Image Guided Neurosurgery ==
== PBMNRRegistration ==
The following is a rough pipeline of the method with proposed classes.
The following is a rough pipeline of the method with proposed classes.
<br><br>
<br><br>
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<b>Data:</b>
<b>Data:</b>
* ?
* Sample dataset for testing.


<b>Distribution:</b>
<b>Distribution:</b>
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* Port significant portions to ITKv4
* Port significant portions to ITKv4
* New ParticleSystem module.  New ITK filter process objects.
* New ParticleSystem module.  New ITK filter process objects.
* Procrustes registration
* Generic infrastructure for point-based surface representations
* Generic infrastructure for point-based surface representations
* Cross-sectional and longitudinal analysis
* Cross-sectional and longitudinal analysis

Latest revision as of 19:41, 27 June 2011

Overlap and Similarity

  • Level of integration questions.
  • Bridges to 3rd party libs
  • Distribution questions: How to build/package with 3rd party libs. How to distribute data.

ITKv4 Features

  • GPU
  • Registration framework

3D Real-Time Physics-Based Non-Rigid Registration for Image Guided Neurosurgery

The following is a rough pipeline of the method with proposed classes.

Contributions: New filters, classes. Application?
Inputs: a segmentation mask, a mesh Outputs: deformation field, transformed image(s)

  1. FeaturePointSelection3dFilter: No dependencies. Plan to start implementation with this filter.
  2. BlockMatching3Dfilter: Similar to Penn FEM registration classes? Perhaps only need to implement a new metric? Plan to use the GPU infrastructure, but also have a non GPU version.
  3. PBMSolver: PETSc dependence
  4. ImageWarp: Already in ITK

What support is needed?

  • CMake integration w/ PETSc and MPI. Build / distribution issues.
  • Further discussion and collaboration with the FEM, registration, and GPU groups.

Gaps:

  • Mesh generation. Tetmesh reader / converter? Use Biomesh3D and bridge to ITK?
  • Self-updating transform object
  • PETSc & MPI within an ITK filter?

Data:

  • Sample dataset for testing.

Distribution: External module?

Lesion Sizing Toolkit

Contributions: Functioning toolkit and application. Possibly new ITK filters classes. Data.

  • Already in ITK as an external module. Contract is to port to use ITKv4 and distribute.
  • Using spatial objects as inputs and outputs

Inputs: DICOM Outputs: Lesion volume measurements and segmentations.

What support is needed?

  • Does ITK want a tighter integration of these classes, and in this same form? Does this cover more general concepts useful to other groups. e.g. Enhanced canny edge detection

Gaps:

  • Representing measures as a concept in ITK
  • Annotations / metadata

Data: 60 datasets. Chest CT scans 1mm resolution. 200mb each. MIDAS? Store as DICOM? Automatically download using CTest.

Distribution: Currently external module. More integration?

ITK Algorithms for Analyzing Time-Varying Shape with Application to Longitudinal Heart Modeling

Contributions: New ITK module (particle system), filters, classes.
Inputs: Segmentations Outputs: Point sets

  • Uses the core of an existing code base built from ITK: ShapeWorks NITRC Repository
  • Port significant portions to ITKv4
  • New ParticleSystem module. New ITK filter process objects.
  • Procrustes registration
  • Generic infrastructure for point-based surface representations
  • Cross-sectional and longitudinal analysis

What support is needed?

  • Logistics of integration and distribution, including data.
  • Future: support for mesh representation, GPU

Data:

  • 25 longitudinal cardiac DE-MRI (1.25mm in-plane, 2.5mm thick) with segmentations of the left atrium. 2-4 datapoints each (pre ablation, 3mo, 6mo, 1 year)
  • Need IRB to release image data

Gaps:

  • Multivariate stats: Bridge to R for complex statistical analysis without going to file system. Only implement what is needed for within ITK algorithms.

Distribution: ITK Module. Test applications. Integration with third party applications (ShapeWorks)