Projects:SegmentationEndocardialWall - Revision history

Ygao at 01:03, 16 November 2013

2013-11-16T01:03:00Z

Ygao at 15:47, 12 January 2012

2012-01-12T15:47:14Z

Ygao: /* Description */

2011-08-01T02:51:26Z

Description

Ygao: /* Description */

2011-03-21T17:39:10Z

Description

Ygao: /* Our Approach */

2011-03-21T16:05:16Z

Our Approach

Ygao: /* Our Approach */

2011-03-21T16:05:03Z

Our Approach

Ygao: /* Our Approach */

2011-03-21T16:04:09Z

Our Approach

Behnood: /* Future Work */

2010-11-12T17:54:03Z

Future Work

Behnood: /* Future Work */

2010-11-12T17:53:24Z

Future Work

Behnood at 17:47, 12 November 2010

2010-11-12T17:47:05Z

@@ Line 1: / Line 1: @@
-  Back to [[Algorithm:BU|Boston University Algorithms]]
+  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
 __NOTOC__
 <gallery>

@@ Line 1: / Line 1: @@
-Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
+ Back to [[Algorithm:BU|Boston University Algorithms]]
 __NOTOC__
 <gallery>

← Older revision		Revision as of 02:51, 1 August 2011
Line 16:		Line 16:

	A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.		A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.
		+
		+	== Slicer Module for Endocardium Segmentation ==
		+	We extract the left atrium endocardium region (blood pool) from the DEMRI image using a multi-atlas based scheme. TODO add more detail and tutorial of using the module.

	== Slicer Module for Wall Segmentation ==		== Slicer Module for Wall Segmentation ==

@@ Line 17: / Line 17: @@
 A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.
-With the endocardiac region having been segmented, we further extract the LA muscle wall by using the coupled surface evolution technique. This algorithm is now implemented as a Slicer module and is available through the extension manager. The module's GUI and one of the results is shown below:
+== Slicer Module for Wall Segmentation ==
 == Future Work ==

@@ Line 19: / Line 19: @@
 With the endocardiac region having been segmented, we further extract the LA muscle wall by using the coupled surface evolution technique. This algorithm is now implemented as a Slicer module and is available through the extension manager. The module's GUI and one of the results is shown below:
-[[Image:LAwallSegmenter.png]]
+[[Image:LAwallSegmenter.png|800px]]
 == Future Work ==

← Older revision		Revision as of 16:04, 21 March 2011
Line 16:		Line 16:

	A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.		A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.
		+
		+	With the endocardiac region having been segmented, we further extract the LA muscle wall by using the coupled surface evolution technique. This algorithm is now implemented as a Slicer module and is available through the extension manager. The module's GUI and one of the results is shown below:
		+	[[Image]]

	== Future Work ==		== Future Work ==

@@ Line 21: / Line 21: @@
 The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately-after-ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation of the left atrial wall, and the blood pool MRA as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involves recurrence of the atrial fibrillation (yes/no), the time of recurrence after the procedure (x number of months), and  any other relevant clinical data which its prediction could assist the physician before the ablation procedure (e.g., the severity of recurrence, etc.). The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.
-The Georgia Tech team proposed using machine learning techniques to predict the success of the ablation procedure and time of recurrence (among other information of interest for the physician) based on enhancements in the left atrial wall in pre-ablation, and immediately-after-procedure DE-MRI's. The feasibility of using pre-ablation MRI for procedure success in already published by the Utah team.
+The Georgia Tech team proposed using machine learning techniques to predict the success of the ablation procedure and time of recurrence (among other information of interest for the physician) based on enhancements in the left atrial wall in pre-ablation, and immediately-after-procedure DE-MRI's. The feasibility of using pre-ablation DE-MRI for procedure success in already published by the Utah team.
 = Publications =

@@ Line 19: / Line 19: @@
 == Future Work ==
-The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately after ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involve recurrence of the atrial fibrillation, the time of recurrence after the procedure, and  any other relevant clinical data which its prediction could assist the physician before the ablation procedure. The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.
+The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately after ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation of the left atrial wall as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involve recurrence of the atrial fibrillation, the time of recurrence after the procedure, and  any other relevant clinical data which its prediction could assist the physician before the ablation procedure. The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.
 The Georgia Tech team proposed using ideas in machine learning to predict the success of the ablation procedure (among other information of interest for the physician) based on pre-ablation MRI. The Utah team has already published results indicating the significance of pre-ablation DE-MRI in the outcome of the ablation therapy.