1. Raj Mudunuri Raj.Mudunuri@iccas.de
Ontological Knowledge Bases for Computer Assisted Surgical Applications
Raj Mudunuri
Innovation Center Computer Assisted Surgery (ICCAS)
Universität Leipzig
Ontologies in Biomedicine and Life Sciences (OBML)
26-27 November 2009
Leipzig

2. Computer Assisted Surgery (CAS)
Monitoring
devices
Radiological
images
Robotic
assistance
systems
Workflow
protocols
Surgical
tools
Tracking
devices
Pathalogy
Anatomy

3. Types of knowledge representation
classical
modern

4. Types of knowledge representation
future

5. Computer Assisted Surgery (CAS)
Monitoring
devices
Radiological
images
Robotic
assistance
systems
Workflow
protocols
Surgical
tools
Tracking
devices
Pathalogy
Anatomy
How to maintain the coherence of this diverse information?

6. Problems and general insights
How was the surgical knowledge represented and used before the advent of CAS, and how is it being processed today?
How can the large amount of heterogeneous data from CAS be represented and processed in the future Operating Room?
Minimise the gap for data-sharing among different information sources within CAS systems
Establish a common minimum knowledge base for these various information sources
Enhance the inter-operability of the data by incorporating semantics into it

7. Our approach
Recognize the various aspects of CAS where ontologies are needed for conceptual description (Anatomy, surgical tools, surgical actions, tracking devices etc.)
Use appropriate programming tools that are expressive enough to model these domains (OWL, SWRL)
Use appropriate querying methods that suit the needs of the applications (SPARQL – for simple rdfs queries, SQWRL – for owl based queries)

8. Methodology of our approach
General Formal Ontology
GFO
(IMISE)
Top-Level ontology
SOCAS (Surgical Ontologies for CAS)
Domain level
core ontology
COCAS
(Core Ontology for CAS)
FESS-ont
NC-Mastoid-ont
Discipline specific
ontologies
Anatomy
Instruments
Activities
RFID
Repository
ontologies

9. Methodology of our approach
cocas:AnatomicalStructure
fess:Polyp
gfo:Material-Object
cocas:SurgicalDevice
fess:Scalpel
gfo:Presential
cocas:OperatingRoom
gfo:Concrete
cocas:Personnel
cocas:Surgeon
gfo:Process
gfo:Individual
cocas:Patient
cocas:OperativePhase
cocas:Functional-MI
cocas:PreOperativePhase
cocas:-Tomographic-MI
cocas:IntraOperativePhase
cocas:MedicalImage
cocas:Microscopy-MI
gfo:Entity
cocas:Optical-MI
cocas:PostOperativePhase
cocas:Pathology
gfo:Category
cocas:FESS
cocas:Surgery
cocas:HNO
cocas:MLS

10. Methodology of our approach
CAS
Application
SQWRL
(for complex conceptual info)
SPARQL
(for simple rdfs)
Ontological KB
(OWL Concepts)
(RDFS/OWL semantics)
(SWRL rules)
Eg. Get the list of entities along with tag ids for objects of the type Knife
Eg. Get the list of concepts based on the entities that are used in application 1 and application 2

11. Testing on simulated environment

12. Testing on simulated environment

13. Testing on simulated environment

14. Conclusions
Designed ontologies (OWL-DL) which can act as conceptual models for few CAS disciplines
Tested their feasibility to be used as background knowledge bases
Further tests need to be done with different models to evaluate the performance of the knowledgebase

15. Thank you Acknowledgement
The Innovation Center Computer Assisted Surgery (ICCAS) at the Faculty of Medicine at the Universität Leipzig is funded by the German Federal Ministry of Education and Research (BMBF) and the Saxon Ministry of Science and the Fine Arts (SMWK) in the scope of the initiative “Unternehmen Region” with the grant numbers 03 ZIK 031, 03 ZIK 032 and 03 ZIK 435.