DBP2:UNCFinal:2010 - Revision history

Grundlett: Text replacement - "http://www.slicer.org/slicerWiki/index.php/" to "https://www.slicer.org/wiki/"

2017-07-10T18:07:06Z

Text replacement - "http://www.slicer.org/slicerWiki/index.php/" to "https://www.slicer.org/wiki/"

Cvachet: /* References */

2011-04-28T14:54:47Z

References

Cvachet: /* References */

2011-04-28T14:51:40Z

References

Cvachet: /* Overview */

2011-03-30T18:52:19Z

Overview

Cvachet: /* Overview */

2011-03-30T18:51:52Z

Overview

Cvachet: /* References */

2011-03-30T18:50:40Z

References

Cvachet: /* References */

2011-03-30T18:50:05Z

References

Medcomparts: /* Overview */

2011-02-01T13:17:27Z

Overview

Medcomparts: /* Overview */

2011-02-01T13:12:44Z

Overview

Medcomparts: /* Overview */

2011-02-01T13:11:50Z

Overview

@@ Line 25: / Line 25: @@
 **Documentation:
 ***[http://www.nitrc.org/plugins/mwiki/index.php/arctic:MainPage NITRC wiki page]
-***[http://www.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]
+***[https://www.slicer.org/wiki/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]
 **Tutorials:
 ***ARCTIC tutorials: [[Media:ARCTIC-Slicer3-Tutorial.ppt|‏ [ppt]]][[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ [pdf]]]
@@ Line 87: / Line 87: @@
 =Related pages=
 *[http://www.slicer.org/pages/Special:SlicerDownloads Slicer 3.6 download]
-*[http://www.slicer.org/slicerWiki/index.php/Documentation-3.6 Slicer 3.6 documentation]
+*[https://www.slicer.org/wiki/Documentation-3.6 Slicer 3.6 documentation]
 *[http://www.niral.unc.edu/ Neuro Image Research and Analysis Laboratory, UNC Chapel Hill]
 *[http://www.cidd.unc.edu/ UNC Carolina Institute for Developmental Disabilities]

@@ Line 93: / Line 93: @@
 =References=
 #<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, Proc. SPIE 7962, 796227 (2011)
-#<cite id="ref_2"></cite>Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet, C., & Piven, J. (in press). Early brain overgrowth in autism results from an increase in cortical surface area before age 2. Arch of Gen Psych.
+#<cite id="ref_2"></cite>Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet, C., & Piven, J., Early brain overgrowth in autism results from an increase in cortical surface area before age 2, Arch Gen Psychiatry. 2011;68(5):1-10
 #<cite id="ref_3"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
 #<cite id="ref_4"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.

@@ Line 92: / Line 92: @@
 =References=
-#<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
+#<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, Proc. SPIE 7962, 796227 (2011)
 #<cite id="ref_2"></cite>Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet, C., & Piven, J. (in press). Early brain overgrowth in autism results from an increase in cortical surface area before age 2. Arch of Gen Psych.
 #<cite id="ref_3"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.

@@ Line 9: / Line 9: @@
 **We performed a longitudinal MRI study to investigate early growth trajectories in brain volume and cortical thickness. Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and then were rescanned approximately 24 months later at age 4-5 years (autism = 38, controls = 21). Results and conclusions should be published soon (paper submitted to AGP, Archives of General Psychiatry).
-*We also developed a novel framework that permits group-wise automatic mesh-based analysis of cortical thickness [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6]]. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module.
+*We also developed a novel framework that permits group-wise automatic mesh-based analysis of cortical thickness [[{{fullurl:{{FULLPAGENAME}}}}#ref_1 1], [{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_6 6], [{{fullurl:{{FULLPAGENAME}}}}#ref_7 7]]. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module.
 **This framework has been tested on a small pediatric dataset and incorporated in an open source C++ based high-level module called GAMBIT. GAMBIT’s setup allows efficient batch processing, grid computing and quality control.

@@ Line 5: / Line 5: @@
 The analysis of neuroimaging data from pediatric populations presents several challenges. Normal variations in brain shape exist from infancy to adulthood as well as normal developmental changes related to tissue maturation. Measuring cortical thickness is one important way to analyze such developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development as reflected in variations in cortical thickness in autistic children and controls.
-*We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4]]. This framework relies first on atlas-based automatic tissue segmentation via an expectation maximization scheme to compute probabilistic and hard tissue segmentations. Second, the skull is stripped by using a post-processed hard tissue segmentation label map as a mask. Third, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. By using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
+*We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_2 2], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4], [{{fullurl:{{FULLPAGENAME}}}}#ref_5 5]]. This framework relies first on atlas-based automatic tissue segmentation via an expectation maximization scheme to compute probabilistic and hard tissue segmentations. Second, the skull is stripped by using a post-processed hard tissue segmentation label map as a mask. Third, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. By using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
 **This framework has been incorporated in our ARCTIC tool, an open source C++ based high-level module disseminated as part of the 3D Slicer toolkit. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware, Inc.).
 **We performed a longitudinal MRI study to investigate early growth trajectories in brain volume and cortical thickness. Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and then were rescanned approximately 24 months later at age 4-5 years (autism = 38, controls = 21). Results and conclusions should be published soon (paper submitted to AGP, Archives of General Psychiatry).

@@ Line 93: / Line 93: @@
 =References=
 #<cite id="ref_1"></cite>C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
-#<cite id="ref_2"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
+#<cite id="ref_2">Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet, C., & Piven, J. (in press). Early brain overgrowth in autism results from an increase in cortical surface area before age 2. Arch of Gen Psych.
-#<cite id="ref_3"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
+#<cite id="ref_3"></cite>Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. [http://www.na-mic.org/publications/item/view/1671 Cortical Correspondence with Probabilistic Fiber Connectivity.] Inf Process Med Imaging. 2009;21:651-63. PMID: 19694301. PMCID: PMC2751643.
-#<cite id="ref_4"></cite>C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract.
+#<cite id="ref_4"></cite>H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
-#<cite id="ref_5"></cite>Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637-1640.
+#<cite id="ref_5"></cite>C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract.
-#<cite id="ref_6"></cite>J. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. [http://www.na-mic.org/publications/item/view/1473 Particle-Based Shape Analysis of Multi-object Complexes.] Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485. PMID: 18979781. PMCID: PMC2753605.
+#<cite id="ref_6"></cite>Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637-1640.

@@ Line 4: / Line 4: @@
 [[Image:BrainDevelopment.jpg|thumb|177px|Brain development]]
-The analysis of neuroimaging data from pediatric populations presents several challenges. Normal variations in brain shape exist from infancy to adulthood as well as normal developmental changes related to tissue maturation. Measuring cortical thickness is one important way to analyze these developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development reflected in variations in cortical thickness in autistic children and controls.
+The analysis of neuroimaging data from pediatric populations presents several challenges. Normal variations in brain shape exist from infancy to adulthood as well as normal developmental changes related to tissue maturation. Measuring cortical thickness is one important way to analyze such developmental tissue changes. As a DBP, our group is interested in the longitudinal study of early brain development as reflected in variations in cortical thickness in autistic children and controls.
 *We first developed a novel framework to perform individual regional cortical thickness analysis [[{{fullurl:{{FULLPAGENAME}}}}#ref_3 3], [{{fullurl:{{FULLPAGENAME}}}}#ref_4 4]]. This framework entails first an atlas-based automatic tissue segmentation via an expectation maximization scheme in order to compute probabilistic and hard tissue segmentations. Secondly, the skull is stripped using a post-processed hard tissue segmentation label map as a mask. Thirdly, a B-spline registration, initialized by centers of mass, rigid then affine registration, is computed to register a T1-weighted atlas to the corrected T1w skull-stripped image. A multi-threaded coarse-to-fine registration scheme using mattes mutual information metric is considered. The full transformation is then applied to a lobar parcellation map defined in the atlas space, dividing the brain into 20+ lobes. Using both the tissue segmentation label map and the registered parcellation map, sparse and asymmetric cortical thickness measurements are finally computed for each lobe.
 **This framework has been incorporated in our ARCTIC tool, an open source C++ based high-level module disseminated as part of the 3D Slicer toolkit. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware Inc).