Difference between revisions of "Algorithm:MGH:New"
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= Overview of MGH Algorithms = | = Overview of MGH Algorithms = | ||
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Cortical Surface Shape Analysis Based on Spherical Wavelets. [[Algorithm:MGH:Development:SphericalWavelets|More...]] | Cortical Surface Shape Analysis Based on Spherical Wavelets. [[Algorithm:MGH:Development:SphericalWavelets|More...]] | ||
| − | <font color="red">'''New: '''</font> | + | <font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007 |
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Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. [[Algorithm:MGH:Development:TopologyCorrection|More...]] | Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. [[Algorithm:MGH:Development:TopologyCorrection|More...]] | ||
| − | <font color="red">'''New: '''</font> | + | <font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007 |
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Our objective is to boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. [[Algorithm:MGH:Development:GroupComp|More...]] | Our objective is to boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. [[Algorithm:MGH:Development:GroupComp|More...]] | ||
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Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|More...]] | Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|More...]] | ||
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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. [[Algorithm:MGH:Development:AutoBrainSeg|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. [[Algorithm:MGH:Development:AutoBrainSeg|More...]] | ||
| − | <font color="red">'''New: '''</font> | + | <font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007 |
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Latest revision as of 06:07, 11 April 2023
Home < Algorithm:MGH:NewBack to NA-MIC Algorithms
Contents
- 1 Overview of MGH Algorithms
- 2 MGH Projects
- 2.1 QDEC: An easy to use GUI for group morphometry studies
- 2.2 Optimal path calculator (Poistats)
- 2.3 Engineering:Project:Non-rigid_EPI_registration
- 2.4 Adding NRRD I/O to Freesurfer
- 2.5 Spherical Wavelets
- 2.6 Topology Correction
- 2.7 QBall Visualization
- 2.8 Tensor-based group comparison (Cramer test)
- 2.9 Numerical Recipies Replacement
- 2.10 Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms
Overview of MGH Algorithms
A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).
MGH Projects
| File:Placeholder.png |
QDEC: An easy to use GUI for group morphometry studiesCompare the primary eigendirection in two groups to see if they are the same.More... New: Put something new here. See: Qdec user page |
| File:Placeholder.png |
Optimal path calculator (Poistats)Provide software deliverable with robust support for input images with varying slice prescription, voxel size, tensor measurement frame, etc. Ensure compatibility with Slicer file formats and Dartmouth tensor data conventions. More... New: Put something new here. |
| File:Placeholder.png |
Engineering:Project:Non-rigid_EPI_registrationMy objective is to evaluate the benefit of using ITK nonlinear registration for group FA comparisons. More... New: Put something new here. |
| File:Placeholder.png |
Adding NRRD I/O to FreesurferOur objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. More... New: Put something new here. |
| File:Placeholder.png |
Spherical WaveletsCortical Surface Shape Analysis Based on Spherical Wavelets. More... New: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007 |
| File:Placeholder.png |
Topology CorrectionGeometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. More... New: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
|
| File:Placeholder.png |
QBall VisualizationOur objective is to visualize q-ball data in Slicer. More... New: Put something new here. |
| File:Placeholder.png |
Tensor-based group comparison (Cramer test)Our objective is to boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. More... New: Paper submitted: "Statistical Group Comparison of Diffusion Tensors via Multivariate Hypothesis Testing." |
| File:Placeholder.png |
Numerical Recipies ReplacementOur obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. More... New: Completed |
| File:Placeholder.png |
Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner PlatformsAtlas-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. More... New: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007 |
