Difference between revisions of "DBP2:Harvard:Brain Segmentation Roadmap"

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== Roadmap ==
 
== Roadmap ==
  
−
  The main goal of this application is to characterize anatomical abnormalities in the  
+
  The main goal of this application is to characterize anatomical connectivity abnormalities in the  
 
  brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia.  
 
  brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia.  
  
−
This page describes the technology roadmap for brain automatic segmentation, using newly acquired 3T data, NAMIC tools and slicer 3.  
+
This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.  
  
−
; A - Optimization of slicer EM white/gray/csf segmentation :
+
; A - Optimization of stochastic tractography algorythm :
−
:* We have been using EM segmenter for our 1.5 Tesla data. This protocol needs to be optimizes for 3T data, adjusting for higher data resolution, different intensity profiles and bias field inhomogeneity. This protocol will be in Slicer 3 (Sylvain, Brad)
+
:* We have been using stochastic tractography for our 1.5 Tesla data, where we traced and analyzed anterior limb of the internal capsule. This algorythm needs to be optimized for 3T data, adjusting for higher data resolution, higher number of diffusion directions and geometric distortions. (Tri, Doug)
−
:** Since technology needed for this project already exists in Slicer 2, its implementation in Slicer 3 is a low risk project, and will be accomplished within the next couple of months. 
+
:* Since the algorythm has been tested on the group data only with respeect to large bundle- internal capsule, it needs to be evaluated when applied to multiple anatomical structures that have smaller sizes, and larger curvature. (Tri, Marek, Doug)
−
:* Since the EM segmentation uses brain atlas, we need to have the technology in place to generate new templates automatically. (Sylvain, Brad, Polina)
+
:** These tests should be completed in relatively short period of time. We will try to optimize the algorythm so that it is ready to run on test dataset, and have results by the Santa Fe tractography meeting (October 2007).   
−
:** This technology should be developed in relatively short period of time. The fact that segmentation atlases will be generated for each study, will ultimately make the technology more robust to other brain diseases.   
+
:** We need good way of segmenting white matter on DTI scans, since stochastic tractography performance depends heavily on good white matter mask. We will look into automatic segmentation provided by slicer2 DTI module, as well as EM segmentation of T2 baseline in cliser 2 (Tri, Brad, Polina, Sylvain).   
−
:** We can discuss the details/use cases at MICCAI.  We should have at least a basic version prepared for the January AHM.  One possibility is to prepare a command line version of the program first and then combine it with Slicer3/add additional functionality after the AHM. We need to keep track of the recent plan to maintain image orientation information within the ITK registration pipeline as this will effect our final product (Brad is checking into this).   
+
; B – Slicer 3 Stochastic Tractography module and testing plus documentation :
−
; B – Segmentation performance comparison, and validation :
+
:* Since this technology already exists in Slicer 2, building the slicer 3 module should be relatively low risk project. We plan to have at least a basic version prepared for the January AHM. In order to ensure proper function of the module, we will test it on the phantom dataset (Tri, Brad). During the January programming week, we plan to finalize slicer 3 module, and work on the softwware documentation.  
−
:* In order to make sure there is no systematic bias between segmentation results of newly acquired 3T data and old 1.5T data, we have chosen 15 control subjects and 15 schizophrenic subjects, which have both scans. We will run and compare results of segmentation, both between methods and within methods between groups.
 
−
:** Since the scanning protocol was established and tested on schizophrenia subjects, and thus data collection is much more advanced there, and since the ultimate goal is to compare anatomical abnormalities in VCFS with these in schizophrenia, this project has two benefits- it gives schizophrenia comparison data, as well as leads to establishing the segmentation protocol that will be easily applicable to VCFS, once more data is collected.  
 
 
; C - Analysis of small anatomical structures :
 
; C - Analysis of small anatomical structures :
−
:* After the protocol for whole brain segmentation is established, small anatomical structures, such as STG, hippocampus, cingulate gyrus, thalamus, caudate and dorsolateral prefrontal cortex will be segmented in both schizophrenia (first) and VCFS (later) (Sylvain, Brad).  
+
:* After the protocol for stochastic tractography is optimized for 3T data, small anatomical structures, such as Uncinate FAsciculus, Arcuate Fasciculus, Fornix will be traced in both schizophrenia (first) and VCFS (later) (Doug, Marek).  
−
:** Technology for segmentation of most of these regions is already in place in Slicer 2 (Sylvain, Kilian). DLPFC module is especially interesting for VCFS population, and this is the first module that will be optimized for our project. 
+
:** Arcuate Fasciuclus is especially interesting for VCFS population, and this is the first tract that will be evaluated using this module and new 3T data.  
−
:** This module is currently being implemented into Slicer3 (John, Brad)
 
−
:*** According to John there is no current plan to transition the module to Slicer3. 
 
−
:*** A Slicer3 version of this module should combine the current Slicer2 rule-based ROI selection with grey matter segmentation from the EMSegmenter module as this segmentation method is more advanced than the current method.  However, this would add to development time.
 
−
:*** In order to adapt this method for new data the segmentation portion of the algorithm must be optimized to the new data. If we use the EMSegmenter then we can leverage our already-planned work of adapting it to the new data.  The rule-based portion of this algorithm is essentially data independent as it only requires the selection of landmarks within images. 
 
−
:* Using segmented DLPFC ROIs, we will perform cortical thickness analysis (Marc, Sylvain). Put the link here [[Algorithm:Harvard:DLPFC Slicer3 Module|link]]
 
−
:** Marc Niethammer has developed a cortical thickness algorithm, which will be put in Slicer 3 [[Algorithm:Harvard:Thickness Slicer3 Module|technique]]
 
 
; D - Subject comparison :
 
; D - Subject comparison :
−
:* Local analysis requires techniques which are not currently in the NA-MIC Kit
+
:* Since the DTI scanning protocol was established and tested on schizophrenia subjects, and thus data collection is much more advanced there, and since the ultimate goal is to compare anatomical connectivity abnormalities in VCFS with these in schizophrenia, this project has two benefits- it gives schizophrenia comparison data, as well as leads to establishing the tractography protocol that will be easily applicable to VCFS, once more data is collected.
−
:** Freesurfer could be used for the local analysis (but it is not in the NA-MIC Kit)
+
:* We alre looking into ways to compare stochastic tractography results between groups. Tract based and volume based measures are considered (both included as parts of the NA-MIC Kit)
−
:** Ipek is developing local analysis tools and may have a tool available in Fall 2008.
+
:** In addition, Ipek is developing local analysis tools and may have a tool available in Fall 2008.
  
 +
  
 
===To do===
 
===To do===
Line 36: Line 29:
 
===Staffing Plan===
 
===Staffing Plan===
  
−
* Sylvain and Marc are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
+
* Katharina, Sylvain, Tri and Doug are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
 
* Polina is the algorithm core contact
 
* Polina is the algorithm core contact
 
* Brad is the engineering core contact  
 
* Brad is the engineering core contact  
Line 42: Line 35:
 
===Schedule===
 
===Schedule===
  
−
* '''12/2007''' - White matter/gray matter/CSF segmentation of the 3T schizophrenia brain using the Slicer 3 EM segment module.
+
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 3T data.
−
* '''12/2007''' - Automatic segmentation of DLPFC in Slicer 2.
+
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
−
* '''01/2008-AHM''' - Comparison between 1.5T vs 3T segmentation performance
+
* '''01/2008-AHM''' - Prototype Stochastic Tractography module in Slicer 3.
−
* '''01/2008-AHM''' - Prototype Automatic atlas generation module in Slicer 3
+
* '''01/2008-AHM''' - Working on the ways of extracting and measuring diffusion properties within the Arcuate Fasciculus using Slicer 3 module.
−
* '''03/2008''' - Cortical thickness measurement of DLPFC using Marc Niethammer's Slicer3 module
+
* '''03/2008''' - Start of the module application to group data.
−
* '''07/2008''' - Local analysis of cortical thickness as a Slicer3 module
+
* '''07/2008''' - BatchMake workflow.
−
* '''10/2008''' - BatchMake workflow
 
 
* '''10/2008''' - Data analysis and paper write up.
 
* '''10/2008''' - Data analysis and paper write up.
−
* '''01/2009-AHM''' - Groupwise local analysis of volumes and cortical thickness as a NA-MIC Workflow
+
* '''01/2009-AHM''' - Groupwise tract based and volume based analysis of the multiple white matter tracts as a NA-MIC Workflow.
  
 
===Team and Institute===
 
===Team and Institute===
 
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
 
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
−
*DBP2 Investigators: Sylvain Bouix, Marc Niethammer
+
*DBP2 Investigators: Sylvain Bouix, Tri Ngo
 
*NA-MIC Engineering Contact: Brad Davis, Kitware
 
*NA-MIC Engineering Contact: Brad Davis, Kitware
 
*NA-MIC Algorithms Contact: Polina Gollard, MIT
 
*NA-MIC Algorithms Contact: Polina Gollard, MIT
  
 
===Links===
 
===Links===

Revision as of 04:55, 19 December 2007

Home < DBP2:Harvard:Brain Segmentation Roadmap

Roadmap

The main goal of this application is to characterize anatomical connectivity abnormalities in the 
brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia.

This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.

A - Optimization of stochastic tractography algorythm 
  • We have been using stochastic tractography for our 1.5 Tesla data, where we traced and analyzed anterior limb of the internal capsule. This algorythm needs to be optimized for 3T data, adjusting for higher data resolution, higher number of diffusion directions and geometric distortions. (Tri, Doug)
  • Since the algorythm has been tested on the group data only with respeect to large bundle- internal capsule, it needs to be evaluated when applied to multiple anatomical structures that have smaller sizes, and larger curvature. (Tri, Marek, Doug)
    • These tests should be completed in relatively short period of time. We will try to optimize the algorythm so that it is ready to run on test dataset, and have results by the Santa Fe tractography meeting (October 2007).
    • We need good way of segmenting white matter on DTI scans, since stochastic tractography performance depends heavily on good white matter mask. We will look into automatic segmentation provided by slicer2 DTI module, as well as EM segmentation of T2 baseline in cliser 2 (Tri, Brad, Polina, Sylvain).
B – Slicer 3 Stochastic Tractography module and testing plus documentation 
  • Since this technology already exists in Slicer 2, building the slicer 3 module should be relatively low risk project. We plan to have at least a basic version prepared for the January AHM. In order to ensure proper function of the module, we will test it on the phantom dataset (Tri, Brad). During the January programming week, we plan to finalize slicer 3 module, and work on the softwware documentation.
C - Analysis of small anatomical structures 
  • After the protocol for stochastic tractography is optimized for 3T data, small anatomical structures, such as Uncinate FAsciculus, Arcuate Fasciculus, Fornix will be traced in both schizophrenia (first) and VCFS (later) (Doug, Marek).
    • Arcuate Fasciuclus is especially interesting for VCFS population, and this is the first tract that will be evaluated using this module and new 3T data.
D - Subject comparison 
  • Since the DTI scanning protocol was established and tested on schizophrenia subjects, and thus data collection is much more advanced there, and since the ultimate goal is to compare anatomical connectivity abnormalities in VCFS with these in schizophrenia, this project has two benefits- it gives schizophrenia comparison data, as well as leads to establishing the tractography protocol that will be easily applicable to VCFS, once more data is collected.
  • We alre looking into ways to compare stochastic tractography results between groups. Tract based and volume based measures are considered (both included as parts of the NA-MIC Kit)
    • In addition, Ipek is developing local analysis tools and may have a tool available in Fall 2008.


To do

  • Assign owners to tasks
  • Define schedule

Staffing Plan

  • Katharina, Sylvain, Tri and Doug are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
  • Polina is the algorithm core contact
  • Brad is the engineering core contact

Schedule

  • 10/2007 - Optimization of Stochastic Tractography algorythm for 3T data.
  • 10/2007 - Algorythm testing on Santa Fe data set and diffusion phantom.
  • 01/2008-AHM - Prototype Stochastic Tractography module in Slicer 3.
  • 01/2008-AHM - Working on the ways of extracting and measuring diffusion properties within the Arcuate Fasciculus using Slicer 3 module.
  • 03/2008 - Start of the module application to group data.
  • 07/2008 - BatchMake workflow.
  • 10/2008 - Data analysis and paper write up.
  • 01/2009-AHM - Groupwise tract based and volume based analysis of the multiple white matter tracts as a NA-MIC Workflow.

Team and Institute

  • PI: Marek Kubicki (kubicki at bwh.harvard.edu)
  • DBP2 Investigators: Sylvain Bouix, Tri Ngo
  • NA-MIC Engineering Contact: Brad Davis, Kitware
  • NA-MIC Algorithms Contact: Polina Gollard, MIT

Links

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