Projects:BayesianMRSegmentation - Revision history

Melonakos: /* Publications */

2010-05-11T20:05:39Z

Publications

Melonakos at 19:53, 20 October 2009

2009-10-20T19:53:20Z

Polina: /* Publications */

2009-09-29T18:58:41Z

Publications

Polina: /* Publications */

2009-09-29T18:57:49Z

Publications

Polina: /* Publications */

2009-09-29T18:56:49Z

Publications

Polina: /* Publications */

2009-09-29T18:55:54Z

Publications

Polina: /* Publications */

2009-09-29T18:53:15Z

Publications

Polina: /* Key Investigators */

2009-09-29T18:52:34Z

Key Investigators

Koen: /* Bayesian Segmentation of MRI Images */

2009-09-10T21:06:17Z

Bayesian Segmentation of MRI Images

Koen: /* Bayesian Segmentation of MRI Images */

2009-09-10T19:12:44Z

Bayesian Segmentation of MRI Images

← Older revision		Revision as of 20:05, 11 May 2010
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	= Publications =		= Publications =

−	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked\| NA-MIC Publications Database]	+	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked\| NA-MIC Publications Database on Bayesian Segmentation of MRI Images]

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 __NOTOC__
 = Bayesian Segmentation of MRI Images =
 The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.
@@ Line 9: / Line 11: @@
 Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.
-= Application to Hippocampal Subfield Segmentation =
+== Application to Hippocampal Subfield Segmentation ==
 We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

← Older revision		Revision as of 18:58, 29 September 2009
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	= Publications =		= Publications =

−	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked\|	+	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked\| NA-MIC Publications Database]
−	NA-MIC Publications Database]

@@ Line 42: / Line 42: @@
 = Publications =
-[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation&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=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked|
 NA-MIC Publications Database]

← Older revision		Revision as of 18:56, 29 September 2009
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	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation&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=Projects%3ABayesianMRSegmentation&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=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked]	+	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked\|NA-MIC Publications Database]

← Older revision		Revision as of 18:55, 29 September 2009
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	[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation&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=Projects%3ABayesianMRSegmentation&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=Projects%3ABayesianMRSegmentation+OR+Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked]

← Older revision		Revision as of 18:53, 29 September 2009
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	= Publications =		= Publications =
−	~~''In Print''~~

−	*[http://www.na-mic.org/publications/pages/display?search=Projects%3ABayesianMRSegmentation&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=Projects%3ABayesianMRSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database]
−
−	~~''In Press''~~
−
−	* Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.
−
−	* Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

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 * MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
-* BWH: Steve Pieper, Ron Kikinis
+* Harvard: Steve Pieper, Ron Kikinis
 = Publications =

@@ Line 3: / Line 3: @@
 = Bayesian Segmentation of MRI Images =
-The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images that automatically adapts to different acquisition sequences. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.
+The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.
 The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.