Projects:ExpectationMaximizationSegmentation - Revision history

Polina at 14:26, 24 April 2009

2009-04-24T14:26:28Z

Sylvainjaume: /* Robust Expectation Maximization Segmentation of MRI Images */

2009-04-23T23:33:37Z

Robust Expectation Maximization Segmentation of MRI Images

Sylvainjaume: /* Robust Expectation Maximization Segmentation of MRI Images */

2009-04-23T23:13:26Z

Robust Expectation Maximization Segmentation of MRI Images

Sylvainjaume at 23:09, 23 April 2009

2009-04-23T23:09:54Z

Sylvainjaume at 23:09, 23 April 2009

2009-04-23T23:09:43Z

Sylvainjaume: /* Expectation Maximization Segmentation of MRI Images */

2009-04-23T23:08:28Z

Expectation Maximization Segmentation of MRI Images

Sylvainjaume: /* Expectation Maximization Segmentation of MRI Images */

2009-04-23T23:07:19Z

Expectation Maximization Segmentation of MRI Images

Sylvainjaume: /* Expectation Maximization Segmentation of MRI Images */

2009-04-23T21:34:59Z

Expectation Maximization Segmentation of MRI Images

Sylvainjaume: /* Expectation Maximization Segmentation of MRI Images */

2009-04-23T18:55:33Z

Expectation Maximization Segmentation of MRI Images

Sylvainjaume: /* Key Investigators */

2009-04-23T18:55:15Z

Key Investigators

@@ Line 1: / Line 1: @@
   Back to [[Algorithm:MIT|MIT Algorithms]]
 __NOTOC__
-= Robust Expectation Maximization Segmentation of MRI Images =
+= Bayesian Segmentation of MRI Images =
 Segmentation algorithms based on the Expectation Maximization (EM) theory

@@ Line 15: / Line 15: @@
 The background of our team encompasses Computer Science and Radiology,
 as well as Research and Industry.
-Our focus will be to identify the bottle necks of existing EM algorithms,
+Our focus will be threefold,
-validate that our method outperfoms existing solutions,
+first to identify the bottlenecks of existing EM algorithms,
-and provide an intuitive implementation to the Research and Clinical Community.
+second to validate the quality of our method on a collection of real life scans,
 ==Key Investigators==

@@ Line 17: / Line 17: @@
 Our focus will be to identify the bottle necks of existing EM algorithms,
 validate that our method outperfoms existing solutions,
-and provide an intuitive implementation to the Research and Clinical Community,
+and provide an intuitive implementation to the Research and Clinical Community.
 ==Key Investigators==

@@ Line 1: / Line 1: @@
-  Back to [[Algorithm:MIT|MIT Algorithms]],
+  Back to [[Algorithm:MIT|MIT Algorithms]]
 __NOTOC__
 = Robust Expectation Maximization Segmentation of MRI Images =

← Older revision		Revision as of 23:09, 23 April 2009
Line 1:		Line 1:
		+	Back to [[Algorithm:MIT\|MIT Algorithms]],
		+	__NOTOC__
	= Robust Expectation Maximization Segmentation of MRI Images =		= Robust Expectation Maximization Segmentation of MRI Images =

← Older revision		Revision as of 23:08, 23 April 2009
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−	= Expectation Maximization Segmentation of MRI Images =	+	= Robust Expectation Maximization Segmentation of MRI Images =

	Segmentation algorithms based on the Expectation Maximization (EM) theory		Segmentation algorithms based on the Expectation Maximization (EM) theory

← Older revision		Revision as of 23:07, 23 April 2009
Line 1:		Line 1:
	= Expectation Maximization Segmentation of MRI Images =		= Expectation Maximization Segmentation of MRI Images =
		+
		+	Segmentation algorithms based on the Expectation Maximization (EM) theory
		+	have proved themselves capable of results of an exceptional quality.
		+	Generally such results were obtained by carefully optimizing the parameters
		+	for a specific MRI protocol and a specific anatomical region.
		+	Besides the segmentation of a standard size MRI scan often requires
		+	a processing time in the order of minutes or hours.
		+	Because of these contraints, EM algorithms have found a limited usability
		+	in the clinical environment.
		+	Our project aims at addressing these issues and designing a new framework
		+	that would be easily trackable by a clinician.
		+	The background of our team encompasses Computer Science and Radiology,
		+	as well as Research and Industry.
		+	Our focus will be to identify the bottle necks of existing EM algorithms,
		+	validate that our method outperfoms existing solutions,
		+	and provide an intuitive implementation to the Research and Clinical Community,
		+
		+	==Key Investigators==
		+	*Sylvain Jaume, MIT
		+	*Koen Van Leemput, MGH
		+	*Polina Golland, MIT
		+	*Ron Kikinis, BWH
		+	*Steve Pieper, BWH

← Older revision		Revision as of 21:34, 23 April 2009
Line 1:		Line 1:
	= Expectation Maximization Segmentation of MRI Images =		= Expectation Maximization Segmentation of MRI Images =
−
−	The goal of this project is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
−
−	The proposed method extends the usual concept of probabilistic atlases in several ways; for instance, it yields sparse tetrahedral meshes that are less prone to overfitting to the training data than traditional atlases. These atlases are therefore better able to predict the antanomy in unseen subjects, especially when the number of training subjects is small.
−
−	A Bayesian modeling approach is used throughout. The first level of Bayesian inference yields a non-rigid group-wise registration algorithm based on a topology preserving deformation prior; the registration criterion is closely related to the so-called congealing criterion. For the higher levels of inference, the method does Bayesian model comparison, which is known to be closely related to the Minimum Description Length principle when Gaussian approximations are used.
−
−	The method explicitly aims at finding the optimal deformation regularization, which involves approximating an integral over all possible deformations. An interesting alternative way to do this, proposed by Stephanie Allassonniere and co-workers, is to side-step the integration by sampling from the deformation posterior in an EM algorithm (although you'd still have to approximate the partition function if the deformation model is not Gaussian, as in our case).
−
−	~~==Key Investigators==~~
−
−	*Sylvain Jaume, MIT
−	*Koen Van Leemput, MGH
−	*Polina Golland, MIT
−	*Ron Kikinis, BWH
−	*Steve Pieper, BWH
−
−	~~==Publications==~~
−
−	~~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, B. Fischl,~~
−	~~Hippocampus, 2009.~~
−
−	~~Encoding Probabilistic Brain Atlases Using Bayesian Inference,~~
−	~~K. Van Leemput,~~
−	~~IEEE Transactions on Medical Imaging, 2009.~~

@@ Line 1: / Line 1: @@
 = Expectation Maximization Segmentation of MRI Images =
-Given a set of manual segmentations, the aim of this paper is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
+The goal of this project is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
 The proposed method extends the usual concept of probabilistic atlases in several ways; for instance, it yields sparse tetrahedral meshes that are less prone to overfitting to the training data than traditional atlases. These atlases are therefore better able to predict the antanomy in unseen subjects, especially when the number of training subjects is small.

@@ Line 11: / Line 11: @@
 ==Key Investigators==
-Sylvain Jaume, MIT
+*Sylvain Jaume, MIT
-Koen Van Leemput, MGH
+*Koen Van Leemput, MGH
-Polina Golland, MIT
+*Polina Golland, MIT
-Ron Kikinis, BWH
+*Ron Kikinis, BWH
-Steve Pieper, BWH
+*Steve Pieper, BWH
 ==Publications==