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All Hands Meeting 2003
Technical Walkthroughs
Mark James
October 9, 2003
Objectives
Make the BIRN user community aware of the technical accomplishments across the BIRN scientific testbeds
Provide an overview of the tools and processes developed
Create a forum for the exchange of new ideas to be tested over the next several months
Each presenter will cover the following key items:• Overview of what was attempted• Review of technical issues• Results/Future
Smart Atlas
Ilya Zaslavsky• Brain Atlases: from images and sketches to interoperable
spatial databases• Haiyun He, Joshua Tran, Maryann Martone
SMaRT: Spatial Markup and Rendering Tool
Setting up atlas sources
Goal: to enable full set of spatial and attribute queries Converting drawings into GIS formats
• Planar enforcement and geometric corrections• Assigning labels to polygons (a multi-step procedure … an arduous
process…)• Creating coordinate system (in SVG: on the fly; in slice shapefiles:
registering to formally-defined stereotaxic coordinates)• Serving as ArcIMS feature services, or shapefiles
Result: spatial database, where most anatomic features have labels (which can be linked to UMLS Ids), and can be served and queried on the Web using a range of spatial and attribute queries
Converting images• Creating tfw files with image coordinates• Serving images via ArcIMS image services• Warping images as necessary
Automatic labeling results
12
3
4
Color shows number of labels assigned to each polygon
Displaying CCDB cell locations
Overlaying images and vector markup from different sources (LONI, Paxinos)
Coordinated display: via UMLS bridge
Coordinated display: via coordinates bridge
UMLS-based interconnections
SMARTAtlas
QueryAtlasbonfire
Spatial or attribute selection UMLS Id
UMLS id
UMLS Id concept Id
concept id
Highlighting slices and areas with the concept Id (or its relatives),
doing literature search
QueryAtlas
SMARTAtlasbonfire
User-defined location concept Id
concept id
concept Id UMLS Id
UMLS id
Displaying slices and areas with the UMLS Id (or its relatives)
Towards an Atlas Environment: Mediator
Smart A Query A . . .
Let’s talk how we can connect !
Skull Stripping Project
Christine Fennema-Notestine • UCSD-fMRI• Other collaborators:
Massachusetts General Hospital Brigham & Women's Hospital UCLA – LONI FMRIB (UK)
Skull Stripping Performance
Manually
Stripped
Images
Outcomes from 3 Different Automated Methods
• Bias Correction: Un-corrected vs. N3 bias-corrected datasets (Sled et al., 1998)
• Different T1-weighted pulse sequences: Contemporary Morphometry BIRN protocol vs. Historical SPGR
• Different Diagnostic Groups:
Diagnostic Group
n Pulse
Sequence Mean Age
(sd)
4 PS#1 35.5 (13.5) Young Controls
4 PS#2 33.0 (15.1)
4 PS#1 75.0 (2.2) Elderly Controls
4 PS#2 74.5 (1.7)
4 PS#1 40.5 (13.3) Unipolar Depressed 4 PS#2 40.8 (10.8)
4 PS#1 76.0 (2.7) Alzheimer’s disease 4 PS#2 75.5 (1.7)
Study Conditions
• Manually stripped slices Two anatomists stripped six sections from each dataset
• Brain Extraction Tool (BET)Smith, 2002; Image Analysis Group, FMRIB, Oxford, UK
• 3dIntracranial (in AFNI)Ward, 1999; NIMH/MCW
• Hybrid Watershed algorithm (in FreeSurfer)Segonne et al., in prep; MGH NMR Center
• Brain Surface Extractor (BSE)Based on Shattuck et al., 2001; USC and LONI, UCLA
Methods Studied
• Significant effect of method: BSE & Hybrid Watershed both more similar to manual outcome, relative to 3dIntra and BET.
• Extent of neurodegeneration across subject groups significantly interacted with automated method. BSE & Hybrid Watershed both more robust across diagnostic group.
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
3dIntra BET BSE Hybrid Watershed
Method
Jacc
ard
Sim
ilar
ity
Ind
ex
YNC
DEPR
ENC
AD
Manual Jaccard Index (0.938)
Results to Date
• Interpretation of complex interactions within Jaccard analysis:
Bias Correction Contemporary BIRN protocol vs. Historical SPGR
• Further analyses with an Expectation/Maximization algorithm to better characterize the differences between methods sensitivity (non-brain removed) specificity (brain kept)
Analyses to Complete
Overall Project Goal
To develop a manuscript for publication that will:
• guide end-users towards a method appropriate to their datasets.
• improve efficiency of processing for large, multi-site neuroimaging studies, such as the BIRN.
• provide insight to the developers for the future.
Query Atlas
Greg Brown• BIRN CC, Brigham and Women’s, UCSD Clinical Imaging
Group
The Challenge of Interpretation
To interpret the results of an imaging study scientists must integrate brain location information with neuroscience information
The voluminous and complex neuroscience literature greatly complicates the process of interpretation.
Interpretation: Two crucial steps
• Relate specific brain locations to the relevant neuroscience literature
• Integrate brain location information and neuroscience information to create new knowledge.
Functions of the QueryAtlas
The QueryAtlas is a knowledge management tool that addresses both steps in the interpretive process
QueryAtlas’ primary methodology involves transforming spatial information (i.e. coordinate information) into anatomical labels that in turn are used to query bibliographic databases and specialized brain atlases.
The output is organized into a variety of knowledge structures
Structure of Query Atlas
QueryAtlas is structured to provide a variety of options for:
• Brain visualization
• Anatomic labeling
• Database and atlas querying
• Output Knowledge Representation
Brain Visualization Options
3D Slicer native viewing mode FreeSurfer Pial Surface FreeSurfer Inflated brain FreeSurfer Spherical brain FreeSurfer MNI Normalized Images AFNI original image brick (after transformation to .cor files)
AFNI Talairached image brick (after transformation to .cor files)
Overlaid functional maps
Example of the Challenge
Labeling Options
MGH Parcellation List
Talairach Daemon Anatomical Labels
Talairach Daemon Brodmann Labels
MGH Labels on FreeSurfer Inflated Brain
Databases and Atlases to Query
Click Search
• Google• Direct PubMed• Arrowsmith augmented search of PubMed• Journal of Neuroscience
Pop-up Window
• Swanson Atlases• Bowden Atlas• PsycInfo
Query Page
Output Knowledge Representations
Output of PubMed (AND, OR) searches by date
Arrowsmith Mediated Outline
Arrowsmith Semantic Filter
A Arrowsmith search based on title words in Medline
Morphology of the frontal operculum:A volumetric magnetic resonance imaging study of the pars triangularis. J Neuroimaging 11:153-9, 2001
Initial magnetic resonance imagingvolumetric brain measurements and outcome in schizophrenia: a prospectiveLongitudinal study with 5-year follow-up.Biol Psychiatry 54:608-15, 2003
A
FrontalOperculum
C
SchizophreniaAn unexpected link
Sets of Medline records. A,C are defined to be disjoint. (Adapted from Swanson, 2003)
3 36
B Magnetic Resonance Imaging
e.g. epilepsy
Arrowsmith Output for Frontal Operculum and Schizophrenia Search
Exploring Large Data Sets
Example: KeyholeSeamless transitions across multiple scales – 7 TB DatabaseIntuitive User InterfaceDisplays where data exists and where notIntegration of Imaging and non-imaging dataMultiple ways to find the data of interest (by feature type, name, address, interactive exploration...)
How to Adapt/Expand this to Medical Images?
Query Atlas Interface Project
An interface to interact with the BIRN database as easily as Keyhole interacts with the earth dataDraw on skills of SDF (award-winning information design firm) in genomics/proteomics visualization
Short-term Goals
a. Complete automated access to Talairach Daemon
b. Explore further FreeSurfer’s capability to display functional statistical maps
c. Develop a pop-up window to access NeuroScholar
d. Use the QueryAtlas to query the Smart Atlas through UMLS IDs available to the Smart Atlas. Homologies between rodent and human brain would be demonstrated for a restricted set of brain regions (e.g. basal ganglia).
e. Move QueryAtlas to UCSD BIRN rack.
f. Write a paper.
Long-term Goals
a. Work with the Small Design Firm to improve the integration of Visual and Text information retrieved from QueryAtlas searches
b. Enhance the two-way interface between the Query and Smart Atlases
c. Add portal to BIRN query tools and databases
d. Explore additional collaborations with University of Southern California and Fox/Lancaster groups.
e. Write a manual.
Imaging Database Web Interface
Burak Ozyurt• UCSD-fMRI
Johnathan Sacks• Surgical Planning Laboratory @ Brigham & Women's
Hospital
Features
UCSD Human Imaging Database /UI current features• Generic assessment query building and result navigation
• Search result export for statistical analysis
• Scan and clinical visit view of a selected subject from the query results.
• Export of the MRI image series as AFNI BRIK (DICOM to AFNI conversion on the fly) from SRB.
• Uses Oracle label security for fine-grained database security
Future work• Derived Data/Data Provenance support
• Ontology / Mediator integration
• Data maintenance through Web UI capabilities
• Additional query wizards and report generators
Architecture
UCSD Human Imaging database Web UI architecture
Oracle Label Security
Assessment Selection
Score Selection
Query Criteria Selection
Query Result
Subject Visit Details
Function BIRN Developments
David Keator• UCI
Functional BIRN DevelopmentsFunctional BIRN Developments
Develop and extend BIRN human Develop and extend BIRN human imaging database schema for the FIRST imaging database schema for the FIRST BIRN.BIRN.
Develop a common data interface for Develop a common data interface for FIRST BIRN image formats using XML.FIRST BIRN image formats using XML.
Develop SRB storage hierarchies and Develop SRB storage hierarchies and upload scripts for FIRST BIRN non-upload scripts for FIRST BIRN non-human and human phantom datasets.human and human phantom datasets.
SubjectsExperiment
Visit
Series
MR DataAssociated Data
Scanner Protocol
ExperimentalProtocol
Database StructureDatabase Structure
Derived Data
AnalysisProtocol
Assessments
Protocols
SRB SRB / HID
XML Common Image FormatXML Common Image Format
Image metadata is preserved in translation using XML!
<bxh> <datarec type="image"> <!--AUTOGEN: Orientation is oblique axial --> <dimension type="x"> <units>mm</units> <size>64</size> </dimension> ... <byteorder>lsbfirst</byteorder> <elementtype>int16</elementtype> <filename>MR6C_20020430_03042_005_00_00001.dcm</filename> <fileoffset>9512</fileoffset> ... <magneticfield>1.5</magneticfield> <description>Head,Obl,2D,GRE</description> <fieldofview>240 240</fieldofview> <tr>1999.984000</tr> <te>40.000000</te></bxh>
Functional BIRN QA Script and XML Functional BIRN QA Script and XML – A Practical Example– A Practical Example
BIRN QA Analysis Pipeline
Analyze7.5
XML-awareBIRN QA
INPUTS
PROCESSINGSTEP
QA plots +image dataOUTPUTS
DICOM GE Pfile
A single BIRN QA script now supports all formats using XML interface!
Format X
add XML add XML add XML add XML
Human SRB TestbedHuman SRB Testbed
Research Project
Institution
Subject
Series
AFNISPMk-SpaceNative BRAINS2…....
XML
Visit
Study
Project ID
BIRN_ID, Gender, Age, AgeQualifier
Institution ID
VisitNumber, Age, SCID, AgeQualifier
Series Instance UID SeriesNumber, Sequence, SequenceVariant, AcqPlane, Matrix_X, Matrix_Y,Matrix_Z, PixelSpacing_X, PixelSpaciny_Y, SliceThickness, FlipAngle
StudyID, StationName, Modality, Weighting,Manufacturer, Model, MRAcquisitionTime
Human SRB Testbed and XMLHuman SRB Testbed and XML
Research Project
Institution
Subject
Series
AFNISPMk-SpaceNative BRAINS2…....
XML
Visit
Study
Future DirectionsFuture Directions
Include extensive front-side and back-side error Include extensive front-side and back-side error checking of both non-human and human data checking of both non-human and human data uploads. uploads.
Develop modularized XML infrastructure, storing Develop modularized XML infrastructure, storing important information at each level of the SRB important information at each level of the SRB hierarchy.hierarchy.
Extend software infrastructure to populate subject, Extend software infrastructure to populate subject, study, and visit metadata and modularized XML study, and visit metadata and modularized XML through clinical database queries.through clinical database queries.
Integrate and co-develop graphical user interfaces Integrate and co-develop graphical user interfaces with the Morphometry and BIRN portal groups for with the Morphometry and BIRN portal groups for database entry, SRB queries, SRB uploads, and database entry, SRB queries, SRB uploads, and SRB downloads.SRB downloads.
Integrate upload scripts into the BIRN portal.Integrate upload scripts into the BIRN portal.
LDMM Data in Morph BIRN SRB
Anthony Kolasny• Johns Hopkins University
Johnathan Sacks• Surgical Planning Laboratory @ Brigham & Women's
Hospital
Vt öt
JHU Tools
LDMM
VTK Conversion
Utility
Shape analysis Vt ötMGH Freesurfer
Images anonymized, segmented,
converted to analyze
MGH-JHU-BWH Pipeline 2003
BIRN SRB Database
BWH Slicer
Visualization tool
Goal: Create tools and procedures that will allow the sharing of data among sites.
Collections
Morphometric BIRNSRB collections and and minimal set of attributes (ldmm output)
Study ID
analyze_rawresultssmoothedraw
registered
Raw- Provided to CIS in May 2003
Analyze_raw cpu: 5 minuteshuman: 1 day real: 1 day
Smoothed cpu: minimalhuman: 45 man hours real: about 3 weeks
Processing Time
Registered cpu: minimalhuman: 20 man hours real: about 2 weeks
Results cpu: 48hrs human: minimal real: 48hrs
We received the data in May and the LDMM processing was complete at the end of July
Collections
Metadata
Morphometric BIRNSRB collections and and minimal set of attributes (ldmm output)
ldmm
hippocampusthalamus
deformation_maps inv_deformation_maps
deformed_target
deformed_template
velocity_fields
KiXmaps{1-n}
KiYmaps{1-n}
KiZmaps{1-n}
HXmaps{1-n}
HYmaps{1-n}
HZmaps{1-n}
velocityX{1-n}
velocityY{1-n}
velocityZ{1-n}
defTarget{1-n}
defTemplate{1-n}
...
Target1 Container Metadata TargetN
template
Atlas
...
AtlasN
raw vtk
deformed_target
deformed_template
velocity_fields
deformation_maps
inv_deformation_maps H-maps{1-n}
defTarget{1-n}
defTemplate{1-n}
Ki-maps{1-n}
velocities{1-n}
Template – will be segmented data set that we wish to compare (this data is local to Subject -> Visit -> Study).
filename{1-n} – represents timestep 1 to n. Each timestep has an file assosiated.
TargetN – will be a link to the nth compared target (data from another visit, study or an Atlas).
movies
Container Metadata
Software
Target
Template
Substructure
Alignment
Hippocampus target deformation animation created using Slicer
MIRIAD Project
MIRIAD: Multisite Imaging Research In the Analysis of Depression
Duke/Harvard (BWH)/UCLA mBIRN Collaboration
MIRIAD Group
Duke University Brigham and Women's UCLA
NIRL SPL LONI
Martha Payne Steve Pieper David Rex
James MacFall Kilian Pohl Arthur Toga
Brian Boyd Neil Weisenfeld
Ranga Krishnan Jonathan Sacks
Douglas McQuoid Simon Warfield
Elizabeth Flint Ron Kikinis
Overview/Rationale Duke Study of Major Depression in Late Life
• Hypotheses relate to effect of vascular lesions on brain/limbic structure and function
• Subjects (age ≥ 60 years) have MRIs at recruitment and every 2 years
• Assessment and care are given in the context of a naturalistic setting of standard-of-care
• Controls are recruited from Duke Center for Aging
Data already analyzed with semi-automated segmentation methods
• suitable for cross-sectional analysis
• difficulties in longitudinal analysis due to method variance
MIRIAD collaboration offers promise of BWH and UCLA tools that offer reduced variance and access to atlas-driven lobar and regional analysis
MIRIAD Plan/Data Flow Use Anonymization at Duke to avoid IRB delays
• select 50 depression subjects, baseline and year 2 MRI
• select 50 age-comparable normal subjects, baseline and year 2 MRI
• select metadata variables that will be needed for analysis
• anonymize data retaining a new BIRN number to link MRI and metadata that cannot be traced to original subject
UCLA Processing • atlas preparation and orientation/registration (rigid
body) of all subjects (also compute HO deformation parameters for segmentation)
• registration of BWH atlas to common data set• after BWH segmentation, perform atlas driven
lobar analysis
BWH Processing• EM Segmentation for gray, white, CSF• Atlas driven regional segmentation
The analysis is splendid demonstration of the promise of the BIRN infrastructure for collaborative studies
MIRIAD Plan/Data Flow
DukeArchives
UCLAAIR Registration
and Lobar Analysis
BWHIntensity Normalizationand EM Segmentation
DukeClinical Analysis
MIRIAD Data Flow1. Retrospective data
upload from Duke2. Lobar analysis and
Registration of Atlas to Subjects
3. Anatomical Segmentation
4. Comparison to Clinical History
1
2
3
4
BWH Probabilistic Atlas(one time transfer)
MIRIAD Status/Plans Status
• Scans uploaded to SRB rack at Duke (Project_0002)
• Metadata set created and anonymized (SAS format)
• UCLA and BWH have performed an analysis on two subjects for final verification of the process (10/6/03)
• Image processing expected to be complete by 10/31/03)
Plans • Each site will have lead author on one of the first four papers
"Senior Author" will be Human Brain Morphometry BIRN Methodology Lobar assessments Longitudinal grey and white matter (baseline vs. yr 2) Regions of Interest
• Improved analysis optimized intensity matching prior to segmentation identification/segmentation of vascular lesions
MIRIAD BIRN Suggestions
Just as there are many image formats, there are many metadata formats. BIRN should more intensively develop guidance/scripts for importing metadata
Sometimes the birn concept seems pretty rigid, e.g., the intermediate results we are generating now to tune the MIRIAD project need to be shared conveniently, but they aren't something we want to archive forever. So far the BIRN tools have been awkward to use in this context (because they are also under development), which is why we've relied on web sites and ftp for moving intermediate data.• Maybe we should have a mechanism so that someone from birn-cc would join
groups as they create their process. They could then help the birn-cc to build tools that facilitate specific projects.
SRB Enhancements
Roman Olschanowsky• BIRN-CC
New Features in SRB
Slsf script – reduces the time for issuing the Sls command Schmod – added a flag “-d” for improving the speed for the
assignment of permissions• You must be the person who originally placed the file in SRB
Error checking script – When using batch scripts for processing files with SRB and an error occurs, it will prevent further processing of the script
bulkUpload script - uses containers for fast data uploading and downloading, the containers are located in /container in a mirrored path of where your files are in /home
wipeallContainers script – used for fast and proper removal of files that were bulk uploaded
SRB Enhancements
Vicky Rowley• BIRN-CC
LUFS and SRB
Linux Userland File System extension for mounting SRB space as a local filesystem
Inserts a module in Linux kernel to handle mount command
Runs a daemon, “lufsd” to intercept commands on mounted directory
srbfs_c is an extension of this software
LUFS Usage
Use lufsmount script or the linux mount command to mount SRB space as part of the local filesystem
Can be configured with suid so each user can mount to their own mountpoint with their own permissions
Each mount operation is performed:• as a specific linux user• as a specific SRB user• with specific Unix permissions• to a specific SRB server/port
LUFS Schematic
Linux
SRB
Local file
system
mounted directory
/home/srb/
samba
W2kmac
Network drive
s:/home/srb/…
(exports only /home/srb)
Linux O/S
Mediator Schematic
Mediator Tools
Registration Tool • Displays all the tables and columns of your database that
are available to be exported. • You can optionally export only those table and columns
that you want BIRN users to query.• Training on the tool is provided by BIRN-CC
Vista • Creates integrated views across columns of data or
multiple sources • Training on the tool is provided by the BIRN-CC
Mediator Tools (continued)
Que• Creates customized queries accessible through the BIRN
Portal so that users can launch their queries against data sources.
• BIRN-CC will provide training on the tool.
Wrapper • The wrapper translates the query request from the
mediator into the correct syntax understood by the data source.
• Wrappers have been developed for Oracle and SRB.• Local configuration of the wrapper will be required for all
data sources. • Configuration support will be done by the BIRN-CC.
BIRN Portal - UMLS
Events Calendar
Message Board
Portal Applications Toolkit