Projects:CardiacAblation - Revision history

Wachinge: /* Description */

2012-11-28T20:28:24Z

Description

Wachinge: /* Description */

2012-11-28T20:27:38Z

Description

Wachinge: /* Description */

2012-11-28T20:27:26Z

Description

Wachinge: /* Description */

2012-11-28T20:26:22Z

Description

Wachinge: /* Description */

2012-11-28T20:21:35Z

Description

Mdepa: /* Results */

2011-03-30T23:26:04Z

Results

Mdepa: /* Left atrium segmentation */

2011-03-30T23:25:27Z

Left atrium segmentation

Mdepa: /* Left atrium segmentation */

2011-03-30T23:25:06Z

Left atrium segmentation

Mdepa: /* Left atrium segmentation */

2011-03-30T23:23:57Z

Left atrium segmentation

Mdepa: /* Results */

2011-03-30T23:21:59Z

Results

@@ Line 8: / Line 8: @@
 We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.
-We also applied our new spectral label fusion algorithm for the segmentation of the cardiac data, with the description of the method and the results at
+We also applied our new spectral label fusion algorithm for the segmentation of the cardiac data, with the description of the method and the results presented at
-[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation  spectral label fusion].
+[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation  Nonparametric Segmentation].
 == Results ==

@@ Line 9: / Line 9: @@
 We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.
 We also applied our new spectral label fusion algorithm for the segmentation of the cardiac data, with the description of the method and the results at
-[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation | spectral label fusion].
+[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation  spectral label fusion].
 == Results ==

@@ Line 9: / Line 9: @@
 We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.
 We also applied our new spectral label fusion algorithm for the segmentation of the cardiac data, with the description of the method and the results at
-[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation| spectral label fusion].
+[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation | spectral label fusion].
 == Results ==

@@ Line 8: / Line 8: @@
 We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.
-[[http://www.na-mic.org/Wiki/index.php/Projects:NonparametricSegmentation]]
+We also applied our new spectral label fusion algorithm for the segmentation of the cardiac data, with the description of the method and the results at
 == Results ==

@@ Line 7: / Line 7: @@
 == Description ==
 We perform the segmentation via a label fusion algorithm [1] that uses a training set of MRA images of different patients with corresponding manual segmentations. We first align the training images to the test subject image to be segmented and apply the resulting deformations to the corresponding manual segmentation label maps to yield a set of left atrium segmentations in the coordinate space of the test subject. These form a non-parametric subject-specific statistical atlas. We then use a weighted voting algorithm to assign every voxel to the left atrium or to the background. The weighted label fusion scheme assigns higher weights to voxels in training segmentations that are located deeper within the structure of interest and that have similar intensities in training and test images. We also handle varying intensity distributions between images by incorporating iterative intensity equalization in a variant of the demons registration algorithm [2] used for the registration of the training images to the novel test image.
 == Results ==

@@ Line 46: / Line 46: @@
 After obtaining the segmentation of the left atrium in the DE-MRI image, we produce a visualization of the ablation scar by simply projecting the DE-MRI data onto the left atrium surface. We restrict the projection to only use image voxels within an empirically determined distance of 7mm of each side of the left atrium surface. Figure 4 below illustrates the maximum intensity projection results for one subject. In addition, we automatically threshold these projection values by computing the 75th percentile and show the resulting visualization as well. For comparison, we also project the expert manual scar segmentation onto the same left atrium surface.
-[[File:Mdepa_scar_visualization.png|500px|thumb|center|Figure 4: Comparison of projections of DE-MRI data and manual scar segmentation onto left atrium surface. Circled area indicates acquisition artifact that causes non-scar tissue to be appear enhanced in the DE-MRI image.]]
+[[File:Mdepa_scar_visualization.png|400px|thumb|center|Figure 4: Comparison of projections of DE-MRI data and manual scar segmentation onto left atrium surface. Circled area indicates acquisition artifact that causes non-scar tissue to be appear enhanced in the DE-MRI image.]]
 We confirm visually that the thresholded projection values correlate well with the manual scar segmentations. Nevertheless, there is one area, which we circled in the figure, where these two differ considerably. This discrepancy is due to an imaging artifact caused by the acquisition protocol and is likely to cause false positives in any intensity-based algorithm.

@@ Line 3: / Line 3: @@
 = Left atrium segmentation =
-Automatic segmentation of the heart’s left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. The high anatomical variability of the heart’s left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.
+Automatic segmentation of the heart’s left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. The high anatomical variability of the left atrium makes its segmentation a particularly difficult problem. Specifically, the shape of the left atrium cavity, as well as the number and locations of the pulmonary veins connecting to it, vary substantially across subjects. We propose and demonstrate a robust atlas-based method for automatic segmentation of the left atrium in contrast-enhanced magnetic resonance angiography (MRA) images.
 == Description ==

@@ Line 48: / Line 48: @@
 [[File:Mdepa_scar_visualization.png|500px|thumb|center|Figure 4: Comparison of projections of DE-MRI data and manual scar segmentation onto left atrium surface. Circled area indicates acquisition artifact that causes non-scar tissue to be appear enhanced in the DE-MRI image.]]
-We confirm visually that the thresholded projection values correlate well with the manual scar segmentations. Nevertheless, there is one area, which we circled in the figure, where these two differ considerably. This discrepancy is due to an imaging artifact caused by the acquisition protocol and is likely to present in any intensity-based algorithm.
+We confirm visually that the thresholded projection values correlate well with the manual scar segmentations. Nevertheless, there is one area, which we circled in the figure, where these two differ considerably. This discrepancy is due to an imaging artifact caused by the acquisition protocol and is likely to cause false positives in any intensity-based algorithm.
 == Conclusions ==