Grid Enabled Collaborative Problem
Solving Environments in Medical
Informatics
Nigel Shadbolt
ECS Southampton
• Collaboration between
elements of the Advanced
Knowledge Technologies
and the Medical Images
and Signals IRC
• MIAKT started Oct 02, 2
years, ~£450K
• EPSRC Core e-Science
Programme
• www.aktors.org/miakt
Dramatis Personae
Institutions
PIs & CIs
RAs
Kings
David Hawkes
Yalin Zheng
Oxford
Mike Brady
Maud Poissonnier
Open Univesity
Enrico Motta
John Domingue
Liliana Cabral
Sheffield
Yorick Wilks
Fabio Ciravegnia
Kalina Boncheva
Southampton
Nigel Shadbolt
Paul Lewis
Srinandan
Dasmahapatra
David Dupplaw
Bo Hu
Multi-disciplinary Assessment
• Multiple
stakeholders
• Multiple
viewpoints and
vocabularies
– Breast imaging –
X-ray, ultrasound,
MRI
– Clinical
examination
– Microscopy –
cells and tissues
(also, hormone
receptors)
– Prognosis,
grading, staging
and statistical
windows into the
future
Why this domain?
• The user and patient perspective
– ≈ 10% of women develop breast cancer during their lives
– 19% cancer deaths, 24% cancer cases
– 3 million screening cases in UK per year: 8~25% cancers are
missed by radiologists; 70~80% biopsies turn out to be benign;
inter- and intra-radiologist variability is typically 30%.
• AKT perspective
– Large amounts of data, information and knowledge
– Collaboration among different domain experts but little technical
support for the collaborative elements of the problem and little
technical support for the semantics of the domain
• MIAS perspective
– Deployment of methods as web and grid services
– Need for information management
MIAKT objectives
• Knowledge acquisition and ontology
development
• Semantic annotation of images
• Annotation based clustering and classification
• Grid and web image processing services
• Language generation
• Deliver information management and services
through “standard” web and internet
connectivity
<Mammo-Abnormality rdf:ID="ROI-0001-0001">
<has-depth rdf:resource='depth-subareolar'/>
<has-morph-feature
<rdf:Description
rdf:ID="ROI-0001-0001">
rdf:resource='shape-mammo-irregular'/>
<rdf:type>
<has-morph-feature
<rdfs:Class rdf:about="#Mammo-Abnormality"/>
rdf:resource='margin-mammo-spiculated'/>
<rdf:Description
rdf:ID="ROI-0001-0001">
</rdf:type>
<is-finding rdf:resource='mass'/>
<rdf:type>
<RDFNsId2:has-depth
rdf:resource='depth-subareolar'
</RDFNsId2:Mammo-Abnormality>
<rdfs:Class
rdf:about="#Mammo-Abnormality"/>
rdf:type='Depth-Descriptor'/>
</rdf:type>
... ...
OWL rdf:resource='depth-subareolar'
<RDFNsId2:has-depth
</RDFNsId2:Mammo-Abnormality>
rdf:type='Depth-Descriptor'/>
... ...
DAML
</RDFNsId2:Mammo-Abnormality>
RDF
MIAKT: Overall Framework and
Current Components
Progress so far: Ontological
Engineering
Ontology Services
•
•
•
Developed and “validated” ontologies for X-ray and MRI mammograms, first
release of histopathology ontology
mappings between conceptual terms and image features
lexical services for report generation
<RDFNsId2:Mammo-Abnormality rdf:about='PE-0001-0001'
RDFNsId2:has-branch='false'
RDFNsId2:has-size='10'
RDFNsId2:graphic-region='Abnorm0'>
<RDFNsId2:has-depth rdf:resource='depth-subareolar'
rdf:type='Depth-Descriptor'/>
<RDFNsId2:has-morph-feature
rdf:resource='shape-mammo-irregular'
rdf:type='Mammo-Shape'/>
Report
<RDFNsId2:has-morph-feature
rdf:resource='margin-mammo-spiculated'
rdf:type='Mammo-Margin'/>
<RDFNsId2:is-finding rdf:resource='mass'
rdf:type='Mass'/>
<RDFNsId2:distri-single-obs
rdf:resource='distri-xray-cluster'
rdf:type='Distribution-Mammo'/>
</RDFNsId2:Mammo-Abnormality>
…contains Abnormality
PE-0001-0001, which is
Mass with a size of
10mm, irregular shape,
spiculated margin, and
with no branches …
Progress so far: Annotation
Enrichment and Annotation Services
• prototype annotation tool developed for navigating ontologies,
delineating image regions and annotating images using descriptors
from the ontology
Progress so far: Image Services 1
• version of a grid enabled image registration
program has been developed
• establishes spatial correspondence by aligning
one image (source image) to another (reference
image) or physical space.
• includes motion correction for dynamic contrast
enhanced breast MR image series.
(a) pre-contrast (b) - post contrast
(c) without
(d) registered.
Progress so far: Image Services 2
Variety of algorithms from Oxford have been wrapped as web services
about to be integrated
–
–
–
–
–
Breast air boundary delineation
Refining the contour of a region of interest interpreted as an abnormality
Analysing the shape of the contour of a region of interest
:
:
Progress so far: Grid and Web
Reasoning Services
• Internet Reasoning Service infrastructure has been developed and
work is in progress to populate it with a range of TA related utilities
• Also feature based API for general service invocation
Progress so far: NLG
• Web service to
support medical
terminology and
ontology mapping and
definitions
• Integration of NL
components to
produce succinct NL
summaries of cases
and annotated
images
Future work: Linking viewpoints
Future work: Intelligent Services
• Classifiers as services to support problem
solving
– Bayesian
– Linear
• Clustering and case based retrieval
• Compendium issue tracking
Lessons from MIAKT
• As a proof of concept MIAKT is powerful and seen as immediately
useful
• MIAKT reuses core components of AKT and MIAS in an e-science
context and has developed significant new capabilities
• However, real users also face really mundane information
management problems
• Globus firewall and security issues defeat fast and realistic
deployment to non sophisticated sites
• General adoption of WSDL API was crucial whilst use of IRS
provided a quick way to incorporate and register services
• Significant issues of privacy and security wrt MIAKT services
• MIAKT in common with many other service oriented projects faces
service tension
– Globus over elaborate for much of what we need
– Web services are defined at low levels of semantic abstraction
• MIAKT faces issue of continuation to sustain progress