Projects:AutomatedShapeModelConstruction - Revision history

Melonakos at 01:27, 28 November 2007

2007-11-28T01:27:12Z

Melonakos at 13:45, 4 September 2007

2007-09-04T13:45:44Z

Melonakos at 13:42, 4 September 2007

2007-09-04T13:42:06Z

Melonakos at 13:41, 4 September 2007

2007-09-04T13:41:34Z

Melonakos at 01:04, 4 September 2007

2007-09-04T01:04:23Z

Melonakos: NA-MIC/Projects/Structural/Shape Analysis/Automated shape model construction moved to Projects:AutomatedShapeModelConstruction

2007-08-21T16:06:25Z

NA-MIC/Projects/Structural/Shape Analysis/Automated shape model construction moved to Projects:AutomatedShapeModelConstruction

Fletcher at 21:51, 19 April 2007

2007-04-19T21:51:32Z

Dougm at 19:48, 18 April 2007

2007-04-18T19:48:36Z

Andy: Update from Wiki

2006-12-18T19:28:40Z

Update from Wiki

New page

Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]

'''Objective:''' We are developing a new method for automatically constructing shape models, including defining boundary correspondences on shapes in a population. This method can be directly applied to segmentations of brain structures from structural MRI.

'''Progress:''' We have implemented a software tool to automatically construct shape models from an input of several binary segmentations. This method finds boundary correspondences by creating the most efficient probability distribution for the population of input shapes. The shape models output by our method can then be used to compare differences in brain structure shape between healthy controls and schizophrenic patients. We are investigating the power of this method in statistical group comparisons.

'''Key Investigators:'''

* Utah: Joshua Cates, Miriah Meyer, Tom Fletcher, Ross Whitaker
* Harvard PNL: Sylvain Bouix, Marc Niethammer, Doug Marchant, Adam Cohen, Mark Dreusicke, Jim Levitt, Martha Shenton

@@ Line 1: / Line 1: @@
 = Automated Shape Model Construction =
 We are developing a new method for automatically constructing shape models, including defining boundary correspondences on shapes in a population. This method can be directly applied to segmentations of brain structures from structural MRI.
-'''Progress'''
+= Description =
 We have implemented a software tool to automatically construct shape models from an input of several binary segmentations. This method finds boundary correspondences by creating the most efficient probability distribution for the population of input shapes. The shape models output by our method can then be used to compare differences in brain structure shape between healthy controls and patients with neuropsychiatric disorders. We are investigating the power of this method in statistical group comparisons.
 {|
@@ Line 22: / Line 19: @@
 Figure 3 shows the raw p-values from hypothesis testing for group differences as color-maps on mean shapes of the autism group (top row) and the normal control group (bottom row).
-''References''
+= Publications =
-'''Key Investigators'''
+[http://www.na-mic.org/Special:Publications?text=Projects%3AAutomatedShapeModelConstruction&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
 * Utah: Joshua Cates, Miriah Meyer, Tom Fletcher, Ross Whitaker
 * Harvard PNL: Sylvain Bouix, Marc Niethammer, Doug Markant, Adam Cohen, Mark Dreusicke, Jim Levitt, Martha Shenton
 * UNC: Martin Styner, Heather Cody Hazlett, Joe Piven

@@ Line 1: / Line 1: @@
 = Automated Shape Model Construction =
-  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
+  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
 '''Objective'''

@@ Line 12: / Line 12: @@
 This method is currently being applied to studying shape changes in the hippocampus and caudate between normal and schizophrenic groups in collaboration with Harvard. We have also extended the method from single figure anatomical objects to a formulation of the multi-object correspondence optimization, and have applied it to a proof-of-concept application to the analysis of brain structure complexes from a longitudinal study of pediatric autism that is underway at UNC Chapel Hill.
 {|
@@ Line 25: / Line 21: @@
 Figure 3 shows the raw p-values from hypothesis testing for group differences as color-maps on mean shapes of the autism group (top row) and the normal control group (bottom row).
 '''Key Investigators'''

@@ Line 16: / Line 16: @@
 * J. Cates, P.T. Fletcher, M. Styner, M. Shenton, R. Whitaker.  Shape Modeling and Analysis with Entropy-Based Particle Systems. IPMI 2007, accepted.
 * J. Cates, P.T. Fletcher, R. Whitaker. Entropy-Based Particle Systems for Shape Corresopndence.  Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006. pp. 90-99 Oct 2006.
 {|
@@ Line 35: / Line 25: @@
 Figure 3 shows the raw p-values from hypothesis testing for group differences as color-maps on mean shapes of the autism group (top row) and the normal control group (bottom row).

@@ Line 1: / Line 1: @@
   Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
-'''Objective:''' We are developing a new method for automatically constructing shape models, including defining boundary correspondences on shapes in a population. This method can be directly applied to segmentations of brain structures from structural MRI.
+'''Objective'''
-'''Progress:''' We have implemented a software tool to automatically construct shape models from an input of several binary segmentations. This method finds boundary correspondences by creating the most efficient probability distribution for the population of input shapes. The shape models output by our method can then be used to compare differences in brain structure shape between healthy controls and patients with neuropsychiatric disorders. We are investigating the power of this method in statistical group comparisons.
+We have implemented a software tool to automatically construct shape models from an input of several binary segmentations. This method finds boundary correspondences by creating the most efficient probability distribution for the population of input shapes. The shape models output by our method can then be used to compare differences in brain structure shape between healthy controls and patients with neuropsychiatric disorders. We are investigating the power of this method in statistical group comparisons.
 This method is currently being applied to studying shape changes in the hippocampus and caudate between normal and schizophrenic groups in collaboration with Harvard. We have also extended the method from single figure anatomical objects to a formulation of the multi-object correspondence optimization, and have applied it to a proof-of-concept application to the analysis of brain structure complexes from a longitudinal study of pediatric autism that is underway at UNC Chapel Hill.
-''References:''
+''References''
 * J. Cates, P.T. Fletcher, M. Styner, M. Shenton, R. Whitaker.  Shape Modeling and Analysis with Entropy-Based Particle Systems. IPMI 2007, accepted.
 * J. Cates, P.T. Fletcher, R. Whitaker. Entropy-Based Particle Systems for Shape Corresopndence.  Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006. pp. 90-99 Oct 2006.
-'''Key Investigators:'''
+'''Key Investigators'''
 * Utah: Joshua Cates, Miriah Meyer, Tom Fletcher, Ross Whitaker
@@ Line 17: / Line 23: @@
 * UNC: Martin Styner, Heather Cody Hazlett, Joe Piven
-'''Links:'''
+'''Links'''
 [[Algorithm:Utah|Utah Algorithms Page]]

← Older revision	Revision as of 16:06, 21 August 2007
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← Older revision		Revision as of 19:48, 18 April 2007
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	* Utah: Joshua Cates, Miriah Meyer, Tom Fletcher, Ross Whitaker		* Utah: Joshua Cates, Miriah Meyer, Tom Fletcher, Ross Whitaker
−	* Harvard PNL: Sylvain Bouix, Marc Niethammer, Doug ~~Marchant~~, Adam Cohen, Mark Dreusicke, Jim Levitt, Martha Shenton	+	* Harvard PNL: Sylvain Bouix, Marc Niethammer, Doug Markant, Adam Cohen, Mark Dreusicke, Jim Levitt, Martha Shenton