Algorithm:UNC:New - Revision history

Styner: /* Overview of UNC Algorithms */

2007-10-30T22:22:38Z

Overview of UNC Algorithms

Ipek: /* Overview of UNC Algorithms */

2007-10-30T11:41:07Z

Overview of UNC Algorithms

Ipek: /* UNC Projects */

2007-10-30T11:37:19Z

UNC Projects

Ipek: /* Shape Analysis Framework using SPHARM-PDM */

2007-10-30T09:26:39Z

Shape Analysis Framework using SPHARM-PDM

Styner: /* Overview of UNC Algorithms */

2007-10-19T19:51:27Z

Overview of UNC Algorithms

Ipek: /* Population Based Correspondence */

2007-10-16T02:40:54Z

Population Based Correspondence

Ipek at 02:34, 16 October 2007

2007-10-16T02:34:29Z

Gcasey at 15:57, 10 October 2007

2007-10-10T15:57:54Z

Gcasey at 15:56, 10 October 2007

2007-10-10T15:56:51Z

Gcasey: /* Quantitative Analysis of Fiber Tract Bundles */

2007-10-10T15:56:24Z

Quantitative Analysis of Fiber Tract Bundles

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 = 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. -DTI-
+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 =

@@ Line 3: / Line 3: @@
 = 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. -cortex- -DTI-
+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. -DTI-
 = UNC Projects =

← Older revision		Revision as of 11:37, 30 October 2007
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	{\|		{\|
		+
		+	\| \| [[Image:Sulcaldepth.png\|thumb\|left\|200px]]
		+	\| \|
		+
		+	== [[Projects:CorticalCorrespondenceWithParticleSystem\|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. [[Projects:CorticalCorrespondenceWithParticleSystem\|More...]]
		+
		+	<font color="red">'''New: '''</font>
		+
		+	* The feature-based particle system implementation successfully works on toy data.
		+	* Currently testing with actual cortical data using sulcal depth.
		+
		+	\|-

	\| \| [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif\|thumb\|left\|200px]]		\| \| [[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.
 * Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)
 |-

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 = 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).
+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. -cortex- -DTI-
 = UNC Projects =

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 * 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.
 |-

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 {|
 | | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|thumb|left|200px]]
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 * Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).
 |}

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 {|
-| style="width:10%" | [[Image:DTIQuantitativeAnalysis.png|thumb|left|200px]]
+| | [[Image:Cbg-dtiatlas-tracts.png|thumb|left|200px]]
-| style="width:90%" |
+| |
-== [[Projects:DTIQuantitativeTractAnalysis|Quantitative Analysis of Fiber Tract Bundles]] ==
+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. [[Projects:DTIPopulationAnalysis|More...]]
-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. [[Projects:DTIQuantitativeTractAnalysis|More...]]
+<font color="red">'''New: '''</font> Command line DTI tools available as part of UNC [http://www.ia.unc.edu/dev NeuroLib]
 |-
-| | [[Image:Cbg-dtiatlas-tracts.png|thumb|left|200px]]
+== [[Projects:DTIQuantitativeTractAnalysis|Quantitative Analysis of Fiber Tract Bundles]] ==
-== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==
+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. [[Projects:DTIQuantitativeTractAnalysis|More...]]
-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. [[Projects:DTIPopulationAnalysis|More...]]
+<font color="red">'''New: '''</font> 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.
 |-