Projects:SegmentationEpicardialWall - Revision history

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2010-02-09T23:31:13Z

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2010-02-08T18:09:29Z

Key Investigators

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Publications

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Our Approach

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Description

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Key Investigators

Behnood at 18:04, 8 February 2010

2010-02-08T18:04:51Z

Melonakos at 19:03, 20 October 2009

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Behnood: /* Publications */

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Publications

← Older revision		Revision as of 23:31, 9 February 2010
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−	~~Back to [[Algorithm:GATech\|Georgia Tech Algorithms]]~~
−	~~__NOTOC__~~
−	~~<gallery>~~
−	~~Image:3D_Segmentation_LA.png \| 3D View of the Segmentation of Endocardial Wall~~
−	~~Image:2d_axial_LA.png \| 2D View~~
−	~~</gallery>~~

−
−	~~= Description =~~
−
−	Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.
−
−	Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.
−
−	~~== Our Approach ==~~
−
−	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.
−
−	~~= Publications =~~
−
−	Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.
−
−	~~= Key Investigators =~~
−
−	~~Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum~~
−
−	~~University of Utah: Rob MacLeod, and Josh Blauer~~

← Older revision		Revision as of 18:09, 8 February 2010
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	Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum		Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum
		+
	University of Utah: Rob MacLeod, and Josh Blauer		University of Utah: Rob MacLeod, and Josh Blauer

← Older revision		Revision as of 18:09, 8 February 2010
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	</gallery>		</gallery>

−
−	~~==Key Investigators==~~
−	* Behnood Gholami, Yi Gao, Wassim M. Haddad, and Allen Tannenbaum, Georgia Tech
−	* Rob MacLeod, and Josh Blauer, University of Utah

	= Description =		= Description =

@@ Line 23: / Line 23: @@
 = Publications =
-''In Press''
+Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.
 = Key Investigators =

@@ Line 19: / Line 19: @@
 == Our Approach ==
-Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.
+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.
 = Publications =

@@ Line 13: / Line 13: @@
 = Description =
-Atrial fibrillation (AF), which is caused by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs can be used to control this condition but it involves side effects and only 40-60% of the AF population is maintained in regular rhythm one year after such treatment. An alternative treatment is catheter ablation in which the sources of electrical dyssynchrony are suppressed by ablating specific tissues using a special catheter. Ablation is performed by exposing the tissue to a high temperature, which results in lesion (scar) formation. This procedure, if successful, provides a final cure to AF.
+Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue.  Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.
-Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.
+Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.
 == Our Approach ==

@@ Line 8: / Line 8: @@
 ==Key Investigators==
-* Behnood Gholami, Yi Gao, Allen Tannenbaum, Georgia Tech
+* Behnood Gholami, Yi Gao, Wassim M. Haddad, and Allen Tannenbaum, Georgia Tech
-* Rob MacLeod, Josh Blauer, University of Utah
+* Rob MacLeod, and Josh Blauer, University of Utah
 = Description =

@@ Line 1: / Line 1: @@
   Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
 __NOTOC__
-= Segmentation of the Epicardial Wall of the Left Atrium =
+<gallery>
-[[Image:afib.JPG|500px]]
 = Description =

@@ Line 12: / Line 12: @@
 == Publications ==
-In Print
+''In Print''
 == Key Investigators ==