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2004 NIH Building on the BIRN
Bruce Rosen, MD PhD
Randy Gollub, MD PhD
Steve Pieper, PhD
http://www.nbirn.net
Morphometry BIRN
What is the Morphometry BIRN? (B. Rosen)
Scientific Background and Significance (R. Gollub)
BIRN Advantages in Morphometry (S. Pieper)
The BIRN Advantage
OUTLINE: Morphometry BIRN
Overall Goal:
Develop capability to analyze and mine data
acquired at multiple sites using processing and
visualization tools developed at multiple sites
Morphometry BIRN
Overall Goal:
Develop capability to analyze and mine data acquired at multiple sites
using processing and visualization tools developed at multiple sites
Context: Human Brain MR Based Morphometry
Initial Application: Alzheimer’s, Depression, Ageing Brain
Participants: BWH, MGH, Duke, UC Los Angeles,
UC San Diego, Johns Hopkins, UC
Irvine,
Washington University
Morphometry BIRN
Human Data Protection
Multi-site data
acquisition
Data Upload
Integration and Application of
Processing Tools
Human Imaging
Database
Morphometry BIRN: Flowchart
Simplified diagram
of building blocks
SRB
Human Data Protection
Multi-site data
acquisition
Data Upload
Integration and Application of
Processing Tools
Human Imaging
Database
Morphometry BIRN: Flowchart
Simplified diagram
of building blocks
SRB
Raw data De-faced data
• De-facing: automated de-facing without brain removal• Pipeline: image formats, BIRN ID generation, defacing, QA, upload
Accomplishment: Developed a robust automated methods for
bulk MRI de-identification and upload to database(diverse inputs, sharable outputs, common
package)
De-identification and Upload Pipeline*
• UCSD (fMRI): A. Bischoff, C.Notestine, B.
Ozyurt , S. Morris, G.G. Brown
• MGH (NMR): B. Fischl
• BWH (SPL): S. Pieper
• UCI: D. Wei
• Duke: B. Boyd
*See demo
Morphometry BIRN: Reality
Human Data Protection
Multi-site data
acquisition
Data Upload
Integration and Application of
Processing Tools
Human Imaging
Database
Morphometry BIRN: Flowchart
Simplified diagram
of building blocks
SRB
Multi-site Structural MRI Data Acquisition & Calibration
Methods: common acquisition protocol, distortion correction, evaluation by scanning human phantoms multiple times at all sites
•MGH (NMR): J. Jovicich, A. Dale, D. Greve, E. Haley
•BWH (SPL): S. Pieper•UCI: D. Keator•UCSD (fMRI): G. Brown •Duke University (NIRL): J. MacFall CorrectedUncorrected
Image intensity variability onsame subject scanned at 4 sites
Morphometry BIRN: Reality
Accomplishment: develop acquisition & calibration protocols that improve reproducibility, within- and across-sites
Human Data Protection
Multi-site data
acquisition
Data Upload
Integration and Application of
Processing Tools
Human Imaging
Database
Morphometry BIRN: Flowchart
Simplified diagram
of building blocks
SRB
Shared Tools for Data Analysis
:
• Freesurfer MGH
• Slicer BWH
• LONI Pipeline UCLA
• LDDMM Johns Hopkins
Morphometry BIRN
Integration and Application of Processing Tools
Various projects driving developments:
• Multi-site Imaging Research in Analysis of Depression*† • Data from one site processed with tools of multiple sites
• Multi-site Morphometry in Analysis of Alzheimer’s Disease *†• Data from multiple sites processed with tools of one site
• Semi-Automated Shape Analysis Project †• Data from BIRN sites processed with tools of various sites
* Demo at BIRN Toolbox’s session (12:00-3:00pm)† Poster available with more details
Morphometry BIRN
Integration and Application of Processing Tools
Projects driving developments:
• Multi-site Imaging Research in Analysis of Depression*† • Data from one site processed with tools of multiple sites
• Multi-site Morphometry in Analysis of Alzheimer’s Disease *†• Data from multiple sites processed with tools of one site
• Semi-Automated Shape Analysis Project †• Data from BIRN sites processed with tools of various sites
* Demo at BIRN Toolbox’s session (12:00-3:00pm)† Poster available with more details
Morphometry BIRN: Reality
MIRIAD Project: Overview
DukeArchives
UCLAAIR Registration
and Lobar Analysis
BWHIntensity Normalizationand EM Segmentation
DukeClinical Analysis
1
2
3
4
BWH Probabilistic Atlas
(one time transfer)
UCSDSupercomputing
Goal: analyze legacy data using automated lobar
segmentation (UCLA) and cortical/subcortical
segmentations (BWH)
MIRIAD Project: Accomplishments
Segmentation Duke BIRN-MIRIAD
Item (semi-automated) (fully-automated)
# of tissue classes 3 (Fig1) 23 (Fig2)
Time for 200 brains 400 hours 1 hour
Time for 200 lobe & 250 hours all lobes (Fig3) and 27 regional analysis regions included above
Improved computational capabilities
1 2 3
Integration and Application of Processing Tools
Projects driving developments:
• Multi-site Imaging Research in Analysis of Depression*† • Data from one site processed with tools of multiple sites
• Multi-site Morphometry in Analysis of Alzheimer’s Disease *†• Data from multiple sites processed with tools of one site
• Semi-Automated Shape Analysis Project †• Data from BIRN sites processed with tools of various sites
* Demo at BIRN Toolbox’s session (12:00-3:00pm)† Poster available with more details
Morphometry BIRN: Reality
AD Project: Overview
MGH Segmentation
Multi-Site Data Acquisition
De-identification and upload
SRB UCSD
BWH/MGH
Multi-site DataQueries and
Statistics
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CVLT Discriminability Score
1000
2000
3000
4000
5000
6000
Left Hippocampal Volume
BWH/MGH and UCSD Data
HID
HID
Visualization and Scientific Search with
3DSlicer & Query Atlas
1
2
3
4
5
AD Project: Accomplishments
• Data sharing:• Successfully tested Deidentification and Upload Pipeline (DUP)
• Integration of data
• Common database schemas for clinical and derived morphometry data at different sites Human Imaging Database (HID)
• Mediated queries that interrogate databases at two sites
• Integration of processing tools
• MGH subcortical segmentation completed on UCSD data
• Statistical tools through the BIRN Portal and HID Query Interface
• Data visualization and interpretation using 3DSlicer and Query Atlas
Integration and Application of Processing Tools
Projects driving developments:
• Multi-site Imaging Research in Analysis of Depression*† • Data from one site processed with tools of multiple sites
• Multi-site Morphometry in Analysis of Alzheimer’s Disease *†• Data from multiple sites processed with tools of one site
• Semi-Automated Shape Analysis Project †• Data from BIRN sites processed with tools of various sites
* Demo at BIRN Toolbox’s session (12:00-3:00pm)† Poster available with more details
Morphometry BIRN: Reality
SASHA Project: Overview
MGH Segmentation
Data DonorSites
De-identificationAnd upload
JHUShape Analysis
of Segmented Structures
SRB
BWHVisualization
Goal: comparison and quantification of structures’
shape and volumetric differences across patient
populations
1
2
3
4
5
UCSDSupercomputing
SASHA Project: Accomplishments
Data: 46 hippocampus data sets (2070 comparisons) Each LDDMM comparison takes about 3 to 8 hours
Large Deformation Diffeomorphic Metric Mapping (LDDMM) using the TeraGrid
Improved computational capabilities
Single PC TeraGrid 1 comparison ~431 days
60 comparisons simultaneously ~7 days
Morphometry BIRN: Future
Calibration: Expand imaging modalities,correction methods, refine protocols
Analysis & Visualization: continue integration development, improve automaticity, support new imaging modalities
Computational Informatics: database interoperability, support genomics, support new image data, grid enable
Utilization: propagate widespread utilization of infrastructure, add new sites to testbed
Why are we here today?
WE NEED FEEDBACK FROM YOU:
Which developments are useful for your projects?
Which developments are we missing?
How can we build better bridges to your projects?
Challenges Technical
• Integration of disparate sources (data and software)
• Processing and handling of large datasets
• Federation of databases in compliance with HIPAA
• Quality control
• Audit and versioning requirements
• Accessing legacy data
• Project coordination and knowledge distillation
Sociological• Encouraging collaboration
• Intellectual Property issues (data & software sharing)
• Authorship
Metrics for Success
Adopted for use by increasing numbers of experts
Sharing of tools and infrastructure with scientific community
Creation and maintenance of a valuable image archive that supports on-going research
Peer reviewed publications in scientific and technical journals
Presentations at national and international scientific meetings
Professional advancement of key personnel linked to success of project
AD Project: Accomplishments
• Successfully tested DUP to share data across sites following federal HIPAA guidelines
• Established common database schemas for clinical and derived morphometry data at different sites (HID)
• Enabled mediated queries that interrogate databases at two sites
• Successfully tested integration of tools for common analysis and data mining
• MGH Subcortical segmentation completed on UCSD data
• Univariate and bivariate statistical tools through the BIRN Portal and HID Query Interface
• Data visualization and intelligent scientific search based on anatomical labels using 3DSlicer and Query Atlas
MIRIAD Project: Accomplishments
• 50 depressed, 50 controls, imaged at baseline and 2 years• Parietal lobe smaller in depressed (p < 0.02)• In subjects responding to therapy:
• Temporal lobe smaller (p < 0.08)• Frontal lobe was not smaller (p < 0.6)