Projects:ParticlesForShapesAndComplexes - Revision history

Marianna: /* Publications */

2010-05-14T13:30:55Z

Publications

Kikinis: /* Publications */

2010-05-11T12:21:21Z

Publications

Kikinis: /* Publications */

2010-05-11T12:20:17Z

Publications

Kikinis: /* Section 5: Correspondence for Multiple-Object Complexes */

2010-05-08T01:51:51Z

Section 5: Correspondence for Multiple-Object Complexes

Cates at 16:12, 7 May 2010

2010-05-07T16:12:12Z

Melonakos at 01:05, 28 August 2009

2009-08-28T01:05:29Z

Melonakos at 17:57, 27 August 2009

2009-08-27T17:57:46Z

Melonakos at 17:57, 27 August 2009

2009-08-27T17:57:10Z

Melonakos at 17:46, 27 August 2009

2009-08-27T17:46:43Z

Melonakos: /* Publications */

2009-07-17T17:26:49Z

Publications

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 = Publications =
-# J Cates, PT Fletcher, and R Whitaker, ''Entropy-based particle systems for shape correspondence,'' in Mathematical Foundations of Computational Anatomy Workshop. 2006, pp. 90–99, MICCAI.
+#Cates J., Fletcher P.T., Whitaker R. Entropy-Based Particle Systems for Shape Correspondence. Int Conf Med Image Comput Comput Assist Interv. 2006;9(WS):90-99.
-# J Cates, P T Fletcher, M Styner, M Shenton, and R Whitaker, ''Shape modeling and analysis with entropy-based particle systems,'' in Information Processing in Medical Imaging (IPMI 2007), LNCS 4584, 2007, pp. 333–345.
+# Cates J., Fletcher P.T., Styner M., Shenton M.E., Whitaker R. Shape Modeling and Analysis with Entropy-Based Particle Systems. Inf Process Med Imaging. 2007;20:333-45.
-# J Cates, P T Fletcher, Z Warnock, and R Whitaker, ''A shape analysis framework for small animal phenotyping with application to mice with a targeted disruption of Hoxd11,'' in Proc. 5th IEEE International Symposium on Biomedical Imaging (ISBI ’08), 2008, pp. 512–516.
+#Cates J., Fletcher P.T., Warnock Z., Whitaker R. A shape analysis framework for small animal phenotyping with application to mice with a targeted disruption of Hoxd11. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 512–516.
-# J Cates, PT Fletcher, and R Whitaker, “A hypothesis testing framework for high-dimensional shape models,” in Mathematical Foundations of Computational Anatomy Workshop. 2008, pp. 170–181, MICCAI.
+#Cates J., Fletcher T., Whitaker R. A Hypothesis Testing Framework for High-Dimensional Shape Models. Mathematical Foundations of Computational Anatomy Workshop. 2008; 170–181.
-# I Oguz, J Cates, P T Fletcher, Z Warnock, R Whitaker, D Cool, S Aylward, and M Styner, “Cortical correspondence using entropy-based particle systems and local featrues,” in Proc. 5th IEEE International Symposium on Biomedical Imaging (ISBI ’08), 2008, pp. 1637–1640.
+#Oguz I., Cates J., Fletcher T., Whitaker R., Cool D., Aylward S., Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.
-# J Cates, PT Fletcher, M Styner, H Hazlett, and R Whitaker, “Particle-based shape analysis of multi-object complexes,” in Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention. (MICCAI 08).
+#Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Particle-Based Shape Analysis of Multi-object Complexes. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.
-* [http://www.na-mic.org/publications/pages/display?search=ParticlesForShapesAndComplexes&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database contents on Particles for Shape and Complexes]
+* [http://www.na-mic.org/publications/pages/display?search=ParticlesForShapesAndComplexes  NA-MIC Publications Database contents on Particles for Shape and Complexes]
 [[Category:Schizophrenia]] [[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Autism]]

@@ Line 80: / Line 80: @@
 # J Cates, PT Fletcher, M Styner, H Hazlett, and R Whitaker, “Particle-based shape analysis of multi-object complexes,” in Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention. (MICCAI 08).
 * [http://www.na-mic.org/publications/pages/display?search=ParticlesForShapesAndComplexes&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database contents on Particles for Shape and Complexes]
 [[Category:Schizophrenia]] [[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Autism]]

@@ Line 80: / Line 80: @@
 # J Cates, PT Fletcher, M Styner, H Hazlett, and R Whitaker, “Particle-based shape analysis of multi-object complexes,” in Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention. (MICCAI 08).
-* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3AParticlesForShapesAndComplexes&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
+* [http://www.na-mic.org/publications/pages/display?search=ParticlesForShapesAndComplexes&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database contents on Particles for Shape and Complexes]
 [[Category:Schizophrenia]] [[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Autism]]

@@ Line 65: / Line 65: @@
 As an experimental analysis of the proposed methodology, we used multi-object segmentation data taken from an ongoing longitudinal pediatric autism study [Hazlett, et al. Arch Gen Psych, V. 62, 2005]. The data consists of 10 subcortical brain structures derived from MRI brain scans of
-autistic subjects (10 samples) and typically-developing controls (15 samples) at 2 years of age (see Fig 6). We sampled each complex of segmentations with 10,240 correspondence points, using 1024 particles per structure. For comparison, we also computed point-correspondence models for each of the 10 structures separately and concatenated their correspondences together to form a marginally optimized joint model. We found significant differences in structure scale only for the right and left amygdala, and no significant group differences in structure position or pose. The hypothesis test method outlined in Section 3 gives a highly significant p-value of 0.0087, which is the first evidence shown for this data for group differences in shape alone (i.e. without scale). By contrast hypothesis testing with the marginally-optimized model showed a significant ''decrease'' in statistical power, with a p-value of 0.0480. Figure 6 shows the mean shape surfaces for the normal and autistic groups, as reconstructed from the Euclidean averages of the correspondence points. Each structure is displayed in its mean orientation, position, and scale in the global coordinate frame. We computed the average orientation for each structure using methods for averaging in curved spaces. The length in the surface normal direction of each of the point-wise discriminant vector components for the autism data is given by the colormap.
+autistic subjects (10 samples) and typically-developing controls (15 samples) at 2 years of age. We sampled each complex of segmentations with 10,240 correspondence points, using 1024 particles per structure. For comparison, we also computed point-correspondence models for each of the 10 structures separately and concatenated their correspondences together to form a marginally optimized joint model. We found significant differences in structure scale only for the right and left amygdala, and no significant group differences in structure position or pose. The hypothesis test method outlined in Section 3 gives a highly significant p-value of 0.0087, which is the first evidence shown for this data for group differences in shape alone (i.e. without scale). By contrast hypothesis testing with the marginally-optimized model showed a significant ''decrease'' in statistical power, with a p-value of 0.0480. Figure 6 shows the mean shape surfaces for the normal and autistic groups, as reconstructed from the Euclidean averages of the correspondence points. Each structure is displayed in its mean orientation, position, and scale in the global coordinate frame. We computed the average orientation for each structure using methods for averaging in curved spaces. The length in the surface normal direction of each of the point-wise discriminant vector components for the autism data is given by the colormap.
 The results of this work are published in [6].

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 = Publications =
 * [http://www.na-mic.org/publications/pages/display?search=ParticlesForShapesAndComplexes&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
 [[Category:Schizophrenia]] [[Category:Shape Analysis]] [[Category:Statistics]] [[Category:Autism]]

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-  Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:Utah|Utah Algorithms]]
+  Back to [[Algorithm:Utah|Utah Algorithms]]
 __NOTOC__
 = Shape Modeling and Analysis with Particle Systems =

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 *In cooperation with scientists and clinicians, we have published several papers that evaluate the above methodologies in the context of biomedical research.
 == Section 1: Particle-Based Correspondence Optimization ==
 The proposed correspondence optimization method is to construct a point-based sampling of a shape ensemble that simultaneously maximizes both the geometric accuracy and the statistical simplicity of the model. Surface point samples, which also define the shape-to-shape correspondences, are modeled as sets of dynamic particles that are constrained to lie on a set of implicit surfaces. Sample positions are optimized by gradient descent on an energy