Projects:BrainManifold - Revision history

Melonakos: /* Publications */

2010-05-11T20:26:38Z

Publications

Sgerber: /* Description */ added small description

2009-10-29T00:00:49Z

Description: added small description

Melonakos at 23:08, 20 October 2009

2009-10-20T23:08:09Z

Sgerber at 18:46, 7 October 2009

2009-10-07T18:46:47Z

Sgerber at 18:46, 7 October 2009

2009-10-07T18:46:24Z

Sgerber at 18:35, 7 October 2009

2009-10-07T18:35:54Z

Sgerber: Stub

2009-10-05T16:22:17Z

Stub

New page

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Brain Manifold Learning =

{|
|[[Image:sgerber_brainmanifold_oasis_manifold.png|thumb|512px|Manifold learned from OASIS database.]]
|}

= Description =

= Key Investigators =

* Utah: Samuel Gerber, Tolga Tasdizen, Sarang Joshi, Tom Fletcher, Ross Whitaker

= Publications =

[[Category:Atlas]] [[Category:Statistics]] [[Category:Registration]] [[Category:Population Analysis]]

@@ Line 23: / Line 23: @@
 ''Published in MICCAI and ICCV''
 * [http://www.cs.utah.edu/~sgerber/research/ Manifold Learning Research Page]
-* [http://www.na-mic.org/publications/pages/display?search=BrainManifold&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=BrainManifold&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Brain Manifold Learning]
 '' In Press ''

@@ Line 20: / Line 20: @@
 = Publications =
 ''Published in MICCAI and ICCV''
 * [http://www.cs.utah.edu/~sgerber/research/ Manifold Learning Research Page]
 * [http://www.na-mic.org/publications/pages/display?search=BrainManifold&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
 [[Category:Statistics]] [[Category:Registration]]

@@ Line 8: / Line 8: @@
 * The low dimensional parametrization simplifies statistical analysis of populations.
 * Applications to searching and browsing large database
-* The manifold represents a localized Atlas. Alternative to template based applications. For example as a segmentation prior.
+* The manifold represents a localized Atlas. Alternative to template based applications, for example as a segmentation prior.
 * Aid in clinical diagnosis. Different regions on the manifold can indicate different pathologies.

← Older revision		Revision as of 18:46, 7 October 2009
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	= Publications =		= Publications =
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−	~~= Publications =~~
−

	''Published in MICCAI and ICCV''		''Published in MICCAI and ICCV''

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 __NOTOC__
 = Brain Manifold Learning =
+This work investigates the use of manifold learning approaches in the context of brain population analysis. The goal is to construct a manifold model from a set of brain images that captures variability in shape, a parametrization of the shape space.
+Such a manifold model is interesting in several ways
+* The low dimensional parametrization simplifies statistical analysis of populations.
-{|
+* Applications to searching and browsing large database
-|[[Image:sgerber_brainmanifold_oasis_manifold.png|thumb|512px|Manifold learned from OASIS database.]]
+* The manifold represents a localized Atlas. Alternative to template based applications. For example as a segmentation prior.
-|}
+* Aid in clinical diagnosis. Different regions on the manifold can indicate different pathologies.
 = Description =
@@ Line 19: / Line 20: @@
 = Publications =
-[[Category:Atlas]] [[Category:Statistics]] [[Category:Registration]] [[Category:Population Analysis]]
+[[Category:Statistics]] [[Category:Registration]]

@@ Line 12: / Line 12: @@
 = Description =
+In many neuroimage applications a summary or representation of a population of brain images is needed. A common approach is to build a template, or atlas, that represents a population. Recent work introduced clustering based approaches, which in a data driven fashion, compute multiple templates  Each template represents a part of the population. In a different direction, researcher proposed kernel-based regression of brain images with respect to an underlying parameter. This yields a continuous curve in the space of brain images that estimates the conditional expectation of a brain image given the parameter.  A natural question that arises based on these investigations is can the space spanned by a set of brain images be approximated by a low-dimensional manifold? In other words, how effectively can a low-dimensional, nonlinear model represent the variability in brain anatomy.
 = Key Investigators =