Projects:LesionSegmentation - Revision history

Melonakos: /* Publications */

2010-05-11T20:30:19Z

Publications

Melonakos at 18:12, 27 August 2009

2009-08-27T18:12:18Z

Melonakos at 18:11, 27 August 2009

2009-08-27T18:11:31Z

Melonakos at 21:10, 26 August 2009

2009-08-26T21:10:06Z

Melonakos at 18:47, 26 August 2009

2009-08-26T18:47:26Z

Prastawa at 23:18, 9 December 2008

2008-12-09T23:18:37Z

Prastawa at 23:15, 9 December 2008

2008-12-09T23:15:06Z

Prastawa at 23:11, 9 December 2008

2008-12-09T23:11:10Z

Prastawa: /* Lesion Segmentation */

2008-12-09T23:04:19Z

Lesion Segmentation

Prastawa: /* Publications */

2008-12-09T22:51:05Z

Publications

← Older revision		Revision as of 20:30, 11 May 2010
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	''In Print''		''In Print''
−	* [http://www.na-mic.org/publications/pages/display?search=LesionSegmentation&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=LesionSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database on Lesion Segmentation]

@@ Line 2: / Line 2: @@
 __NOTOC__
-== Lesion Segmentation ==
+= Lesion Segmentation =
 = Description =

@@ Line 2: / Line 2: @@
 __NOTOC__
-= Lesion Segmentation =
+== Lesion Segmentation ==
 Quantification, analysis and display of brain pathology such as white matter lesions as observed in MRI is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes and for developing new therapies. Utah Center for Neuroimage Analysis develops new methodology for extraction of brain lesions from volumetric MRI scans and for characterization of lesion patterns over time. The images show white matter lesions (yellow) displayed with ventricles (blue) and transparent brain surface in a patient with an autoimmune disease (lupus). Lesions in white matter and possible correlations with cognitive deficits are also studied in patients with multiple sclerosis (MS), chronic depression, Alzheimer’s disease (AD) and in older persons.
@@ Line 18: / Line 20: @@
 [[Image:prastawa-lesionest.png|thumb|center|400px|An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data. Left: initial guess. Center: final estimate. Right: observed patient data. Top row: the T2 intensities, bottom row: lesion probabilities]]
-= Publications =
+*Utah Algorithms: Marcel Prastawa, Guido Gerig
 = Publications =
@@ Line 25: / Line 30: @@
 ''In Print''
 * [http://www.na-mic.org/publications/pages/display?search=LesionSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]

@@ Line 21: / Line 21: @@
 = Publications =
-* Marcel Prastawa and Guido Gerig. Brain Lesion Segmentation through Physical Model Estimation. International Symposium on Visual Computing (ISVC) 2008. Lecture Notes in Computer Science (LNCS) 5358, Pages 562-571.
+= Publications =
-* Marcel Prastawa and Guido Gerig. Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic Region Partitioning. 3D Segmentation in the Clinic: A Grand Challenge II Workshop at Medical Image Computing and Computer Assisted Intervention (MICCAI) 2008. Insight Journal.
 = Key Investigators =

← Older revision		Revision as of 18:47, 26 August 2009
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−	Back to [[Algorithm:Utah2\|Utah 2 Algorithms]]	+	Back to [[Algorithm:Utah2\|Utah 2 Algorithms]]
	__NOTOC__		__NOTOC__

← Older revision		Revision as of 23:18, 9 December 2008
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	In addition to the identification of the location and shape of lesions in 3D, we are interested in analyzing the longitudinal series of brain MRI showing lesions. For this purpose, we have developed a method for estimating a physical model for lesion formation. The model that we use is an approximation using a reaction-diffusion process that is based on expected diffusion properties (as observed through DTI). This approach gives a richer parametrization of lesion changes in addition to volume and location, as the model estimation provides descriptions of growth and spread for individual lesions. In the future, we plan to incorporate this approach for analyzing lesion MRI of a subject over time by characterizing the change patterns through the physical model parameters.		In addition to the identification of the location and shape of lesions in 3D, we are interested in analyzing the longitudinal series of brain MRI showing lesions. For this purpose, we have developed a method for estimating a physical model for lesion formation. The model that we use is an approximation using a reaction-diffusion process that is based on expected diffusion properties (as observed through DTI). This approach gives a richer parametrization of lesion changes in addition to volume and location, as the model estimation provides descriptions of growth and spread for individual lesions. In the future, we plan to incorporate this approach for analyzing lesion MRI of a subject over time by characterizing the change patterns through the physical model parameters.

−	[[Image:prastawa-lesionest.png\|thumb\|center\|400px\|An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data.]]	+	[[Image:prastawa-lesionest.png\|thumb\|center\|400px\|An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data. Left: initial guess. Center: final estimate. Right: observed patient data. Top row: the T2 intensities, bottom row: lesion probabilities]]

@@ Line 6: / Line 6: @@
 Quantification, analysis and display of brain pathology such as white matter lesions as observed in MRI is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes and for developing new therapies. Utah Center for Neuroimage Analysis develops new methodology for extraction of brain lesions from volumetric MRI scans and for characterization of lesion patterns over time. The images show white matter lesions (yellow) displayed with ventricles (blue) and transparent brain surface in a patient with an autoimmune disease (lupus). Lesions in white matter and possible correlations with cognitive deficits are also studied in patients with multiple sclerosis (MS), chronic depression, Alzheimer’s disease (AD) and in older persons.
-{|
+<center>
 |[[Image:prastawa-lupus-demo001.png|thumb|256px|Segmentation of a lupus case with large lesions.]]
 |[[Image:prastawa-lupus-case001-3T.png|thumb|256px|Segmentation of a 3T lupus case with small lesions.]]
 |-
 |}
 In addition to the identification of the location and shape of lesions in 3D, we are interested in analyzing the longitudinal series of brain MRI showing lesions. For this purpose, we have developed a method for estimating a physical model for lesion formation. The model that we use is an approximation using a reaction-diffusion process that is based on expected diffusion properties (as observed through DTI). This approach gives a richer parametrization of lesion changes in addition to volume and location, as the model estimation provides descriptions of growth and spread for individual lesions. In the future, we plan to incorporate this approach for analyzing lesion MRI of a subject over time by characterizing the change patterns through the physical model parameters.
-{|
+[[Image:prastawa-lesionest.png|thumb|center|400px|An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data.]]
-[[Image:prastawa-lesionest.png|thumb|center|256px|An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data.]]
 = Publications =

@@ Line 12: / Line 12: @@
 |}
-In addition to the identification of the location and shape of lesions in 3D, we are interested in analyzing the longitudinal series of brain MRI showing lesions. For this purpose, we have developed a method for estimating a model for lesion formation. The model that we use is an approximation using a reaction-diffusion process that is based on expected diffusion properties (as observed through DTI).
+In addition to the identification of the location and shape of lesions in 3D, we are interested in analyzing the longitudinal series of brain MRI showing lesions. For this purpose, we have developed a method for estimating a physical model for lesion formation. The model that we use is an approximation using a reaction-diffusion process that is based on expected diffusion properties (as observed through DTI). This approach gives a richer parametrization of lesion changes in addition to volume and location, as the model estimation provides descriptions of growth and spread for individual lesions. In the future, we plan to incorporate this approach for analyzing lesion MRI of a subject over time by characterizing the change patterns through the physical model parameters.
-[[Image:prastawa-lesionest.png|An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data.]]
+{|
 = Publications =

@@ Line 1: / Line 1: @@
- Back to [[Algorithm:Utah2|Utah 2 Algorithms]]
+Back to [[Algorithm:Utah2|Utah 2 Algorithms]]
 __NOTOC__
@@ Line 14: / Line 14: @@
 = Publications =
 * Marcel Prastawa and Guido Gerig. Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic Region Partitioning. 3D Segmentation in the Clinic: A Grand Challenge II Workshop at Medical Image Computing and Computer Assisted Intervention (MICCAI) 2008. Insight Journal.