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Capturing and Modeling
Neuro-Radiological
Knowledge on a
Community
Basis:
The Head Injury Scenario.
Alexander Garcia, Zhuo Zhang,
Menaka Rajapakse, Christopher J. O. Baker,
and Suisheng Tang
Data Mining Department
Institute for Infocomm Research
Singapore
Outline
•
•
•
•
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Motivation
MiBank – Head Injury Database
Ontology Development
Collective Intelligence
The “facebook” approach
Medical Image Annotator
Discussion and Conclusions
Motivation
• National Neurological Institute Singapore (NII) has 500+
head injury patients each year with Brain, Scalp, Skull,
Internal bleeding requiring rapid diagnosis.
• Clinical radiology reports comprise of multiple series of
Computed Tomography (CT) Images with unstructured text
associated to images)
• Computationally a weak association between images and
words, cannot retrieve similar images.
• Conceptually a tightly coupled association between Image
and Diagnosis
• MiBank database of DICOM files,
(http://dicom.i2r.a-star.edu.sg/pacsone/)
 Browser
Features of MiBank
 Search by category,
patient, report, note, study
 Annotation with free-text
 Forum discussion
 DICOM viewer
 Image upload & download
MiBank:
Medical Image Databank: Head Injury
Web site: http://dicom.i2r.a-star.edu.sg/pacsone/
505 studies, 1775 series, 31561 images. Pass word protect, DICOM viewer, searchable
Possible
query in
MiBank
Show me all cases who have skull fracture with acute subdural
hematoma But do not have brain edema.
Details in next page
Impossible
query with nopredefined
terms
Show all cases who have skull fracture with midline shift and
acute subdural hematoma But do not have brain edema.
Current Limitations of MiBank
 Can not query based on image features explicitly;
 Can not associate the description in R-report to specific instance of an image.
Need to see all instances for bone fracture
Sample: Radiology Report
 A fracture of the right frontal bone.
 Mild midline shift to the left is present.
 An acute extradural hematoma, measuring
1.9 cm in maximal thickness, is noted.
 A 1 cm thick acute subdural hematoma is
also present over the right cerebral
hemisphere.
What do we want… What do we need?
• Retrieve patients with
right midnight shifts of
less than 3mm for
whom there has been
no reported
haematoma
• Retrieve all images
similar to this one
• Properly annotated data:
images, radiology reports
• Meaningful associations
between reports, images,
and across images
• …an ontology ….
Header Info
Mining
Original
DICOM
data
Semantic
Query
Ontology
Categorization
Image
retrieve
Indexing
Text mining
Search
Engine
Visualization
Web Interface
Report
data
Head Injury
database
(Relational)
Customized
online report
Statistic
report
Discussion
forum
The Role of the Ontology
• Community defined controlled vocabulary for
annotation of radiology images.
• Hierarchical descriptions of medical terms relevant to
anatomy, pathology and head injury specific features
found in medical images.
• Consensus model of head injury terminology
generated through community engagement for
knowledge reuse in medical information systems.
• Query model for semantic search
Ontology Development
Garcia et al
Ontology Development
Ph ase 1
Phase 2
Text Processing / Baseline Ontology
FMA
Non FMA
“Plain scans were acquired. Note is made of
the MRI dated 2/3/2004 and CT dated
18/2/2004.Evidence of previous left high
parietal craniectomy noted. Hypodensity
in the left parietal-occipital region is
compatible with gliosis at site of previous
surgery. A large left-sided scalp hematoma
is seen. Underlying linear radiolucency in
the left frontal bone was seen. This
suggests
an
undisplaced
fracture.
Underlying acute subdural hematoma is
seen with a maximal depth of 1.2 cm. Acute
subarachnoid blood is also noted
collecting mainly in the ipsilateral
cerebral hemisphere, sylvian fissure as
well as tentorium. There is diffuse cerebral
edema. Mass-effect is seen with midline
shift to the right, and developing
hydrocephalus. Basal cisterns are
effaced”.
FMA / Galen / R-report terms:
anatomy, pathology, trauma, injury
Capturing Knowledge: Phase 1
Not an easy task
• Inside expert’s head
• Difficult to describe
concepts and relations
• Difficult for nonexperts to understand.
Disadvantages
• Requires excessive amount of time
• Experts – easily bored – no short term
result.
• Results in the creation of unstructured
knowledge stores that are difficult to
reuse and maintain.
• Skimping on validation may include
errors, omissions, inconsistencies &
irrelevances
• Experts are not always capturing the
evidence – rather explaining context
• Storing the knowledge that is not
machine-readable
Ontology Development
Maintenance
Evolution
Ph ase 1
Phase 2
Capturing Knowledge: Phase 2
• Knowledge Elicitation via
Collective Intelligence
• Collective Knowledge
Resources
– The capacity to provide
useful information based on
human contributions which
gets better as more people
participate.
– intelligent collection?
– Data Types
• mix of structured,
machine-readable data
and unstructured data
from human input
– what we all know but
hadn’t got around to
saying in public before
• collaborative bookmarking,
searching
– “database of intentions”
• clicking, rating, tagging,
buying - Amazon
• blogs, wikis,
discussion lists -
facebook
Retrieving images of the
diving trip to Australia.
Albert and Alex have to
be in the photo.
• The Premise:
From unstructured and
unrelated annotation to
structured meaningful
annotation
• Simple tagging it
possible to derive
meaningful associations
• Need to have a tool to
gather knowledge that
is directly linked to
supporting evidence.
Tags Make The Difference !
Medical Image Annotator: MIA
•
Main challenge in medical image retrieval
is that it heavily depends on expert’s
knowledge of data structures and
annotation is poor. So the objective of MIA
is knowledge capture.
•
MIA is designed for medical image
annotation and its users are domain experts
who require a consistent vocabulary for
annotation tasks, knowledge sharing and
machine automation.
•
User community consists of Radiologists,
Neurosurgeons (specifically, NNI doctors).
Medical students, junior doctors, image
processing researchers.
•
MIA is a designed to both facilitate the
building of appropriate ontology by
domain experts and effective maintenance
and evolution of the ontology,
given
new use cases /images.
MIA User Interface
Our contribution: the use of WEB 2.0
technology to support knowledge capture,
and the approach to community
engagement in the development of the
ontology; more concretely in the
maintenance and evolution
MIA: Platform Architecture
Easy to extend, any OWL file can be loaded
Ontologies can be edited online:
* add node * rename node * delete node
Ajax to update ontologies on server side
to provide dynamic content on a web page
so no page-refresh, no re-loading
Image
.owl
file
OntologyEditor
AJAX
Database
Owl
Parser
Tree
Constructor
Java script
(DHTML)
Ontology & Image
Management
Console
OntologyViewer
Server-side processors
OWL files can be loaded dynamically
OWL  relational database  OWL
Client-side browser
• Users can keep their own version of ontology
• Consolidated ontology will be generated based on
community inputs.
Knowledge Capture in Action
Knowledge Capture in Action
Knowledge Capture in Action
Medical Image Annotator: MIA
•
•
•
•
•
•
Advantages
Fast and easy
Domain experts lead the
process
Always rooted in reality
or a medical use case
Maintenance and
evolution of the
controlled vocabulary is
assured.
Excellent training for
new doctors / radiologists
Facilitates Data Mining
of Radiology reports
Ontology Evolution
• Different trainee and
clinical doctors building
ontologies with
extensions on
different sub trees
• Consolidated ontology
is currently manually
curated
• Goal is automatically
align & merge ontologies
Query with the Head Injury Ontology
1. Simple ‘ontology-term’ assisted query
•
•
Search for images: based merely on simple
combination of ontology terms (and / or)
Form based interface linked to SQL Queires
2. Ontology reasoning (A-box)
•
•
Content navigation over R-reports using defined
object properties (Knowlegtor)
Use of subsumption and object properties
Head Injury Ontology
Concepts
(27)
Roles
(38)
• BrainRegion
• has_BrainRegion
Information
• Symptom
• has_Symptoms
• DiseaseDiagnosis
• has_DiseaseDiagnosis
• DiseaseStage
• has_DiseaseStage
• ImageReport
• has_ImageReport
• ImageView
• has_ImageView
• TextReport
• has_TextReport
• Intracranial
Hemorrhage
• has_Intracranial
Haemorrhage
• Intraventricular
Hemorrhage
• has_Intrventricular
Haemorrhage
Find patient records for ‘Fracture’
Discussion and Conclusions
• Medical images should be better annotated in order to facilitate
information retrieval
• Collective knowledge is real… “FAQ-o-Sphere”
• Controlled vocabularies (CVs) and/or ontologies are being
developed by communities
• Simple tagging combined with knowledge elicitation methods
supports ontology development
• Collective knowledge capture requires dedicated infrastructure
that supports specific tasks
• Querability can be improved through the use of explicit tags
and CVs/ontologies
Challenges for the Community
• How to get knowledge from all those intelligent
people on the Internet
• How to give everyone the benefit of everyone
else’s experience
• How to leverage and contribute to the ecosystem
that has created today’s web.
Social Web
Life Science
Social + Semantic Web
Acknowledgments
• Bonarges Aleman-Meza – Social Web
• Tom Gruber - Semantic-Social Web
• MIA Developers - Zhang Zhuo and Menaka
Rajapakse
• Suisheng Tang M.D. and Project PI, - Coordinator
of domain experts and builder of baseline ontology
• Tchoyoson Lim – Radiologist NNI (National
Neuroscience Institute, Singapore)