NAMIC Wiki - User contributions [en]

Projects:AtlasBasedBrainSegmentation

2011-12-09T04:22:15Z

Phz: /* Description */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Atlas-Based Brain Segmentation =

{|
|[[Image:combined_50_seg_labeled.png|thumb|512px|Coded edge map and the resulting segmentations]]

|}

= Description =
Atlases are widely used to aid in brain segmentation or tissue classification, they are typically constructed by summarizing information concerning image intensities and anatomical shapes from a set of training images that includes manual segmentations. Conventional atlas building methods rely on the averaging process of all training images, which can lead to significant loss of information. Recent research shows that selecting a subset of atlases that are similar to the test subject provides more accurate segmentation than those given by randomly selected atlases.

We proposed a fast,shape-based hierarchical matching approach based on Spatial Pyramid Matching (SPM) to perform a very efficient k-NN search for testing brains. We comparied the proposed method against known methods for k-NN lookup and evaluated the atlases built on these methods on a training set contains 259 brain MRIs and a testing set of 20 brain MRIs.

= Key Investigators =

* Utah: Peihong Zhu, Suyash P. Awate, Ross Whitaker

= Publications =

* P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

[[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Segmentation]]

Projects:AtlasBasedBrainSegmentation

2011-12-09T04:21:37Z

Phz: /* Atlas-Based Brain Segmentation */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Atlas-Based Brain Segmentation =

{|
|[[Image:combined_50_seg_labeled.png|thumb|512px|Coded edge map and the resulting segmentations]]

|}

= Description =
Atlases are widely used to aid in Brain segmentation or tissue classification, they are typically constructed by summarizing information concerning image intensities and anatomical shapes from a set of training images that includes manual segmentations. Conventional atlas building methods rely on the averaging process of all training images, which can lead to significant loss of information. Recent research shows that selecting a subset of atlases that are similar to the test subject provides more accurate segmentation than those given by randomly selected atlases.

We proposed a fast,shape-based hierarchical matching approach based on Spatial Pyramid Matching (SPM) to perform a very efficient k-NN search for testing brains. We comparied the proposed method against known methods for k-NN lookup and evaluated the atlases built on these methods on a training set contains 259 brain MRIs and a testing set of 20 brain MRIs.

= Key Investigators =

* Utah: Peihong Zhu, Suyash P. Awate, Ross Whitaker

= Publications =

* P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

[[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Segmentation]]

Projects:AtlasBasedBrainSegmentation

2011-12-09T04:18:34Z

Phz: Created page with ' Back to Utah Algorithms __NOTOC__ = Atlas-Based Brain Segmentation = {| |[[Image:combined_50_seg_labeled.png|thumb|512px|Coded edge map and the resulting …'

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Atlas-Based Brain Segmentation =

{|
|[[Image:combined_50_seg_labeled.png|thumb|512px|Coded edge map and the resulting segmentations|}

= Description =
Atlases are widely used to aid in Brain segmentation or tissue classification, they are typically constructed by summarizing information concerning image intensities and anatomical shapes from a set of training images that includes manual segmentations. Conventional atlas building methods rely on the averaging process of all training images, which can lead to significant loss of information. Recent research shows that selecting a subset of atlases that are similar to the test subject provides more accurate segmentation than those given by randomly selected atlases.

We proposed a fast,shape-based hierarchical matching approach based on Spatial Pyramid Matching (SPM) to perform a very efficient k-NN search for testing brains. We comparied the proposed method against known methods for k-NN lookup and evaluated the atlases built on these methods on a training set contains 259 brain MRIs and a testing set of 20 brain MRIs.

= Key Investigators =

* Utah: Peihong Zhu, Suyash P. Awate, Ross Whitaker

= Publications =

* P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

[[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Segmentation]]

Algorithm:Utah

2011-12-09T04:13:17Z

Phz:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|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...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|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. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of
Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2011-12-09T04:10:42Z

Phz:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|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...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|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. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of
Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2011-12-09T04:07:30Z

Phz:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|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...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|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. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of
Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of segmentations of each brains in the dataset, to build an accurate atlas for the target.

|}

Algorithm:Utah

2011-12-09T04:06:08Z

Phz:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
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== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

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== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

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== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

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== [[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...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

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== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

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== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|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. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

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== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

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== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of
Nonregular Shapes, MICCAI 2011.

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== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

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== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

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== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

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== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of segmentations of each brains in the dataset, to build an accurate atlas for the target.

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2011-12-09T04:00:20Z

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