Algorithm:MGH - Revision history

JChris.FillionR: Fix MediaWiki table formatting issue discovered while converting to GitHub Flavored Markdown using pandoc (via https://github.com/outofcontrol/mediawiki-to-gfm)

2023-04-11T06:04:31Z

Fix MediaWiki table formatting issue discovered while converting to GitHub Flavored Markdown using pandoc (via https://github.com/outofcontrol/mediawiki-to-gfm)

Melonakos at 18:36, 4 November 2009

2009-11-04T18:36:24Z

Melonakos at 18:06, 4 November 2009

2009-11-04T18:06:31Z

Melonakos at 17:59, 20 October 2009

2009-10-20T17:59:04Z

Melonakos at 19:55, 2 September 2009

2009-09-02T19:55:29Z

Melonakos at 19:52, 2 September 2009

2009-09-02T19:52:09Z

Melonakos at 01:25, 28 August 2009

2009-08-28T01:25:48Z

Melonakos: /* Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms */

2009-08-26T15:30:12Z

Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms

Melonakos: /* Numerical Recipies Replacement */

2009-08-26T15:29:59Z

Numerical Recipies Replacement

Melonakos: /* Topology Correction */

2009-08-26T15:29:39Z

Topology Correction

@@ Line 21: / Line 21: @@
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 || [[Image:lh.pm14686.BA2.gif|250px]]
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 Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage. 2009 Oct 15;48(1):21-8.
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 <font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, P. Golland, B. Fischl. Task-Optimal Registration Cost Functions. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.
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← Older revision		Revision as of 18:06, 4 November 2009
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	See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]		See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]
		+
		+	\|-
		+
		+	{\| cellpadding="10" style="text-align:left;"
		+	\|\| [[Image:lh.pm14686.BA2.gif\|250px]]
		+	\|\|
		+
		+	== [[Projects:LearningRegistrationCostFunctions\| Learning Task-Optimal Registration Cost Functions]] ==
		+
		+	We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions\|More...]]
		+
		+	<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, P. Golland, B. Fischl. Task-Optimal Registration Cost Functions. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.

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@@ Line 8: / Line 8: @@
 {| cellpadding="10" style="text-align:left;"
-| style="width:15%" |
+|| [[Image:Salatneuroimage09fig1.png‎|250px]]
-| style="width:85%" |
+||
 == [[Projects:ShapeBasedLevelSetSegmentation|Shape Based Level Segmentation]] ==

@@ Line 8: / Line 8: @@
 {| cellpadding="10" style="text-align:left;"
-| style="width:15%" | [[Image:qdec.jpg|200px]]
+| style="width:15%" |
 | style="width:85%" |
-== [[Projects:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==
+== [[Projects:ShapeBasedLevelSetSegmentation|Shape Based Level Segmentation]] ==
-Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream.  The functionality in Qdec is also available as a processing module within Slicer3, and XNAT. [[Projects:QDEC|More...]]
+This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Projects:ShapeBasedLevelSetSegmentation|More...]]
-See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]
+<font color="red">'''New: '''</font> Salat D.H., Lee S.Y., van der Kouwe A.J., Greve D.N., Fischl B., Rosas H.D.
 |-
-| |
+| | [[Image:qdec.jpg|200px]]
 | |
-== [[Projects:ShapeBasedLevelSetSegmentation|Shape Based Level Segmentation]] ==
+== [[Projects:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==
-This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Projects:ShapeBasedLevelSetSegmentation|More...]]
+Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream.  The functionality in Qdec is also available as a processing module within Slicer3, and XNAT. [[Projects:QDEC|More...]]
-<font color="red">'''New: '''</font> Salat D.H., Lee S.Y., van der Kouwe A.J., Greve D.N., Fischl B., Rosas H.D.
+See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]
 |-

← Older revision		Revision as of 19:52, 2 September 2009
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	See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]		See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]
		+
		+	\|-
		+
		+	\| \|
		+	\| \|
		+
		+	== [[Projects:ShapeBasedLevelSetSegmentation\|Shape Based Level Segmentation]] ==
		+
		+	This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Projects:ShapeBasedLevelSetSegmentation\|More...]]
		+
		+	<font color="red">'''New: '''</font> Salat D.H., Lee S.Y., van der Kouwe A.J., Greve D.N., Fischl B., Rosas H.D.
		+	Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage. 2009 Oct 15;48(1):21-8.

	\|-		\|-

← Older revision		Revision as of 01:25, 28 August 2009
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	Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Projects:AutomaticFullBrainSegmentation\|More...]]		Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Projects:AutomaticFullBrainSegmentation\|More...]]
		+
		+	\|-
		+
		+	\| \| [[Image:GT-SulciOutlining1.jpg\|200px]]
		+	\| \|
		+
		+	== [[Projects:SulciOutlining\|Automatic Outlining of sulci on the brain surface]] ==
		+
		+	We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining\|More...]]

	\|}		\|}

← Older revision		Revision as of 15:30, 26 August 2009
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	Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Projects:AutomaticFullBrainSegmentation\|More...]]		Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Projects:AutomaticFullBrainSegmentation\|More...]]
−
−	~~<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007~~

	\|}		\|}

← Older revision		Revision as of 15:29, 26 August 2009
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	Our objective is to replace algorithms using the proprietary Numerical Recipes for C source base in FreeSurfer in the efforts to open-source FreeSurfer. This project has been completed through the use of the open source packages VXL (VNL) and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 25 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008. [[Projects:FreeSurferNumericalRecipiesReplacement\|More...]]		Our objective is to replace algorithms using the proprietary Numerical Recipes for C source base in FreeSurfer in the efforts to open-source FreeSurfer. This project has been completed through the use of the open source packages VXL (VNL) and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 25 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008. [[Projects:FreeSurferNumericalRecipiesReplacement\|More...]]
−
−	~~<font color="red">'''New: '''</font> Completed~~

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← Older revision		Revision as of 15:29, 26 August 2009
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	Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator. [[Projects:TopologyCorrectionNonSeparatingLoops\|More...]]		Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator. [[Projects:TopologyCorrectionNonSeparatingLoops\|More...]]
−
−	~~<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007~~

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