Difference between revisions of "DBP2:UNC:Cortical Thickness Roadmap"
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Next we discuss the main modules and details of current status and development work: | Next we discuss the main modules and details of current status and development work: | ||
− | + | === White matter/gray matter segmentation === | |
:* UNC has a segmentation technique implemented in an ITK framework for segmenting white matter and gray matter in the young brain. This technique will be converted into a Slicer3 [[Slicer3:Execution Model Documentation | command line module]] | :* UNC has a segmentation technique implemented in an ITK framework for segmenting white matter and gray matter in the young brain. This technique will be converted into a Slicer3 [[Slicer3:Execution Model Documentation | command line module]] | ||
:** Since this segmentation technique exists in an ITK framework, the integration into Slicer3 is low risk and should be completed over the next couple of months (mid fall) | :** Since this segmentation technique exists in an ITK framework, the integration into Slicer3 is low risk and should be completed over the next couple of months (mid fall) | ||
Line 21: | Line 21: | ||
:** This will be a good test case for applying the Slicer3 EM Segment module to a slightly different application. UNC should work through the training material on the Slicer3 EM Segment module and then refer to Brad Davis and Kilian Pohl as needed. | :** This will be a good test case for applying the Slicer3 EM Segment module to a slightly different application. UNC should work through the training material on the Slicer3 EM Segment module and then refer to Brad Davis and Kilian Pohl as needed. | ||
:** This also should be completed before the AHM. | :** This also should be completed before the AHM. | ||
− | + | === Local cortical thickness measurement === | |
:* UNC has an algorithm to measure local cortical thickness given a labeling of white matter and gray matter. This technique will be converted into a Slicer3 command line module | :* UNC has an algorithm to measure local cortical thickness given a labeling of white matter and gray matter. This technique will be converted into a Slicer3 command line module | ||
:** This technique is non-symmetric and sparse (only computing distances where they can be computed reliably). | :** This technique is non-symmetric and sparse (only computing distances where they can be computed reliably). | ||
:** It is expected that this module should be available before the AHM. | :** It is expected that this module should be available before the AHM. | ||
:* Marc Niethammer was developing a [[Algorithm:Harvard:Thickness Slicer3 Module|technique]] at a previous project week that would be symmetric. This could be an alternative used as a comparison. | :* Marc Niethammer was developing a [[Algorithm:Harvard:Thickness Slicer3 Module|technique]] at a previous project week that would be symmetric. This could be an alternative used as a comparison. | ||
− | + | === Local correspondence === | |
:* Regional as well as local subject comparisons are needed | :* Regional as well as local subject comparisons are needed | ||
:* Regional analysis will require precise deformable registration to a young brain atlas | :* Regional analysis will require precise deformable registration to a young brain atlas | ||
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:** Freesurfer could be used for the local analysis (but it is not in the NA-MIC Kit) | :** Freesurfer could be used for the local analysis (but it is not in the NA-MIC Kit) | ||
:** Ipek is developing local analysis tools and may have a tool available in Fall 2008. | :** Ipek is developing local analysis tools and may have a tool available in Fall 2008. | ||
− | + | === Hypothesis testing === | |
− | + | === Performance characterization and validation === | |
:* Characterize response based on signal noise, patient motion, etc. | :* Characterize response based on signal noise, patient motion, etc. | ||
:* Comparison to other tools (FreeSurfer) | :* Comparison to other tools (FreeSurfer) | ||
− | ===To do=== | + | === To do === |
* Assign owners to tasks | * Assign owners to tasks | ||
* Define schedule | * Define schedule | ||
Line 44: | Line 44: | ||
− | ===Schedule=== | + | === Schedule === |
* '''xx/xx/2007''' - White matter/gray matter segmentation of the young brain using UNC technique as a Slicer3 module | * '''xx/xx/2007''' - White matter/gray matter segmentation of the young brain using UNC technique as a Slicer3 module | ||
Line 57: | Line 57: | ||
− | ===Team and Institute=== | + | === Team and Institute === |
*Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099) | *Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099) | ||
*Co-PI: Joseph Piven, MD | *Co-PI: Joseph Piven, MD | ||
*NA-MIC Engineering Contact: Jim Miller, GE Research | *NA-MIC Engineering Contact: Jim Miller, GE Research | ||
*NA-MIC Algorithms Contact: Martin Styner, UNC | *NA-MIC Algorithms Contact: Martin Styner, UNC |
Revision as of 19:20, 25 September 2007
Home < DBP2:UNC:Cortical Thickness RoadmapContents
Objective
We would like to create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow applied to the young brain (2-4 years old) is one goal of the UNC DBP. This page describes the technology roadmap for cortical thickness analysis in the NA-MIC Kit. The basic components necessary for this end-to-end application are:
- Tissue segmentation: Should be multi-modality, correcting for intensity inhomogeneity and work on non-skull-stripped data
- Cortical thickness measurement: Local cortical thickness needs measurements at every location of the white-gray matter boundary, as well as at the gray-csf boundary. Regional analysis does not need such a dense measurement.
- Cortical correspondence: Local analysis needs a full correspondence on both white-gray boundary and gray-csf boundary.
- Statistical analysis/Hypothesis testing: Measurements need to be compared and tested localy incorporating multiple-comparison correction, correlative analysis would be necessary too.
Roadmap
Starting with several MRI images (weighted-T1, weighted-T2...) we want to obtain cortical thickness maps for each subject, compute cortical correspondences between subjects, and analyze the cortical thickness at these corresponding locations. Ultimately, the NA-MIC Kit will provide a workflow for individual and group analysis of cortical thickness. It will be implemented as a set of Slicer3 modules that can be used interactively within the Slicer3 application as well as in batch on a computing cluster using BatchMake.
Next we discuss the main modules and details of current status and development work:
White matter/gray matter segmentation
- UNC has a segmentation technique implemented in an ITK framework for segmenting white matter and gray matter in the young brain. This technique will be converted into a Slicer3 command line module
- Since this segmentation technique exists in an ITK framework, the integration into Slicer3 is low risk and should be completed over the next couple of months (mid fall)
- UNC will also investigate adapting the Slicer3 EM Segment module to their young brain studies. Here, UNC will adapt the UNC atlas of the 2 year old brain to provide priors for the EM Segment module
- This will be a good test case for applying the Slicer3 EM Segment module to a slightly different application. UNC should work through the training material on the Slicer3 EM Segment module and then refer to Brad Davis and Kilian Pohl as needed.
- This also should be completed before the AHM.
- UNC has a segmentation technique implemented in an ITK framework for segmenting white matter and gray matter in the young brain. This technique will be converted into a Slicer3 command line module
Local cortical thickness measurement
- UNC has an algorithm to measure local cortical thickness given a labeling of white matter and gray matter. This technique will be converted into a Slicer3 command line module
- This technique is non-symmetric and sparse (only computing distances where they can be computed reliably).
- It is expected that this module should be available before the AHM.
- Marc Niethammer was developing a technique at a previous project week that would be symmetric. This could be an alternative used as a comparison.
- UNC has an algorithm to measure local cortical thickness given a labeling of white matter and gray matter. This technique will be converted into a Slicer3 command line module
Local correspondence
- Regional as well as local subject comparisons are needed
- Regional analysis will require precise deformable registration to a young brain atlas
- NA-MIC Kit tools can be applied here
- Local analysis requires techniques which are not currently in the NA-MIC Kit
- Freesurfer could be used for the local analysis (but it is not in the NA-MIC Kit)
- Ipek is developing local analysis tools and may have a tool available in Fall 2008.
Hypothesis testing
Performance characterization and validation
- Characterize response based on signal noise, patient motion, etc.
- Comparison to other tools (FreeSurfer)
To do
- Assign owners to tasks
- Define schedule
Schedule
- xx/xx/2007 - White matter/gray matter segmentation of the young brain using UNC technique as a Slicer3 module
- xx/xx/2007 - White matter/gray matter segmentation of the young brain using the Slicer3 EM Segment module
- xx/xx/2007 - Cortical thickness measurement using UNC technique as a Slicer3 module
- xx/xx/2007 - Cortical thickness measurement using Marc Niethammer's approach as a Slicer3 module
- xx/xx/2007 - Deformable registration of young brain regional atlas
- xx/xx/2007 - Regional analysis of cortical thickness as a Slicer3 module
- xx/xx/2007 - BatchMake workflow
- xx/xx/2007 - Groupwise regional analysis of cortical thickness as a NA-MIC Workflow
- xx/xx/200x - Groupwise local analysis of cortical thickness as a NA-MIC Workflow
Team and Institute
- Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099)
- Co-PI: Joseph Piven, MD
- NA-MIC Engineering Contact: Jim Miller, GE Research
- NA-MIC Algorithms Contact: Martin Styner, UNC