Difference between revisions of "Algorithm:UNC:New"

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= Overview of UNC Algorithms =
 
= Overview of UNC Algorithms =
  
A brief overview of the UNC's algorithms goes here. This should not be much longer than a paragraph.  Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects.  The projects below are organized into a two column table:  the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table.  You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).
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At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We are now working on incorporating various data sources on the entire cortical surface for improving the correspondence computation.
  
 
= UNC Projects =
 
= UNC Projects =
  
 
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== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==
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In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]
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<font color="red">'''New: '''</font>
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* The feature-based particle system implementation successfully works on toy data.
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* Currently testing with actual cortical data using sulcal depth.
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| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|thumb|left|200px]]
 
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* First version of Shape Analysis Toolset available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) , this is to be added to the NAMIC toolkit.
 
* First version of Shape Analysis Toolset available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) , this is to be added to the NAMIC toolkit.
* Toolset distribution contains open data for other researcher to evaluate novel shape analysis enhancements
 
* Slicer 3 modules for individual tools in the UNC Shape Analysis Toolset completed
 
 
* Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)
 
* Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)
* New shape analysis related papers:
 
** 2 submissions to the Main MICCAI 2007 conference
 
  
 
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* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])
 
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])
 
* Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.
 
* Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.
* Submission to MICCAI 2007 conference
 
  
 
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Latest revision as of 22:22, 30 October 2007

Home < Algorithm:UNC:New

Back to NA-MIC Algorithms

Overview of UNC Algorithms

At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We are now working on incorporating various data sources on the entire cortical surface for improving the correspondence computation.

UNC Projects

Sulcaldepth.png

Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. More...

New:

  • The feature-based particle system implementation successfully works on toy data.
  • Currently testing with actual cortical data using sulcal depth.
UNCShape OverviewAnalysis MICCAI06.gif

Shape Analysis Framework using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. More...

New:

  • First version of Shape Analysis Toolset available as part of UNC Neurolib open source (download) , this is to be added to the NAMIC toolkit.
  • Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)
UNCShape ShapeCorrespondence.png

Population Based Correspondence

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. More...

New:

  • Software available as part of UNC Neurolib open source (website)
  • Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.
UNCShape CaudatePval MICCAI06.png

Local Statistical Analysis via Permutation Tests

We have further developed a set of statistical testing methods that allow the analysis of local shape differences using the Hotelling T 2 two sample metric. Permutatioin tests are employed for the computation of statistical p-values, both raw and corrected for multiple comparisons. Resulting significance maps are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Ongoing research focuses on incorporating covariates such as clinical scores into the testing scheme. More...

New:

  • Available as part of Shape Analysis Toolset in UNC Neurolib open source (download).
Cbg-dtiatlas-tracts.png

Population Analysis from Deformable Registration

Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. More...

New: Command line DTI tools available as part of UNC NeuroLib


DTIQuantitativeAnalysis.png

Quantitative Analysis of Fiber Tract Bundles

DT-MRI tractography can be used as a coordinate system for computing statistics of diffusion tensor data. The quantitative analysis of diffusion tensors takes into account the space of tensor measurements using a nonlinear Riemannian symmetric space framework. Tracts of interest are represented as a medial spline attributed with cross-sectional statistics. More...

New: Gilmore J, Lin W, Corouge I, Vetsa Y, Smith J, Kang C, Gu H, Hamer R, Lieberman J, Gerig G. Early Postnatal Development of Corpus Callosum and Corticospinal White Matter Assessed with Quantitative Tractography. AJNR Am J Neuroradiol. 2007.

DTINoiseStatistics.png

Influence of Imaging Noise on DTI Statistics

Clinical acquisition of diffusion weighted images with high signal to noise ratio remains a challenge. The goal of this project is to understand the impact of MR noise on quantiative statistics of diffusion properties such as anisotropy measures, trace, etc. More...

New: Casey Goodlett, P. Thomas Fletcher, Weili Lin, Guido Gerig. Quantification of measurement error in DTI: Theoretical predictions and validation, MICCAI 2007.