Ontology-based Medical Imaging and Radiology Informatics ...Presentation Transcript
Ontology-based Medical Imaging and Radiology Informatics — Domain Mapping and Data Modeling Dr. Jun Ni, Associate Professor, Director Medical Imaging HPC and Informatics Lab Department of Radiology Carver College of Medicine The University of Iowa, Iowa City, Iowa, USA Shanghai Jiao Tong University MedicalDec. 14, 2009 School, 21 Century Forum
OutlineOntological Considerations of Medical ResearchDomain Emphasizing on the medical domain mapping and its role in today’s medical multidisciplinary researchMedical Imaging Informatics Domain Scope and ModelConsiderations Focusing on domain and data model distinctions and challengesProjects in MHHI Lab at the University of Iowa Spotlighting some of relevant projects which reflects our vision and task actions
Ontology vs. Medical ResearchWhat is ontology? Philology’s Sub-specialty (theoretical and practical) Existing, Entity (naming, definition, catalog) and RelationshipsDomain? Area with a scope or boundary Special academic discipline or field Interdisciplinary or multidiscipline It has belongings (e.g., medical physics vs. physical medicine)Domain ontology? Domain entity, domain model, scope, boundary, crossing, object and service model, class, object, services, process, … Each entityMedical domain and medical research domain
Medical and Medical Research DomainsMedical Domain Medical (Clinic) service domain (Healthcare) Patient-oriented service operations (hospital or clinic services) Business operations Healthcare industrials (modality) interactions Social and government policy making and implementation Healthcare information system (HIS)
Medical and Medical Research DomainMedical Domain Medical research domain (Science) “Medical” as the leading entity Interaction with scientific research (including biological science, biochemistry, biophysiology, biophysics, computer science, and engineering fields Biomedical research domain (biomedical science and biomedical engineering) Biomedical research and biomedical engineering Difference between sciences (explorations and discoveries) and engineering areas (implementation and integral efforts) Clinical translational or transformative efforts (linking to medical service domain) CTSA’s roadmap
Medical Domain Medical Clinic Service Medical Research Domain Domain (Healthcare services, …)Clinical TranslationalExperiments and Science Clinical(CTSA in USA) Transformative
Medical Clinic Service Clinical Medical Research Domain Domain Translational DomainInternal Medicine CT, MRI, PET, Innovative Anatomical science, Surgery X-ray modality, medical physics, Radiology Mammon graph, Diagnostics, medical imaging, Interventional, CAD, image processing, Neurology Breast imaging, Imaging image storage, IT- Whole body informatics, based healthcare Cardiology imaging, Physiological systems, medical Pathology Neuoradiology, Anatomic imaging informatics, … analysis, diagnostic analysis,Gastroenterology medical physics, … … Dermatology Obstetrics and Nanotechnology, Gynecology Chemotherapy, biotransport, Nano- thermo-science, radiation therapy, Oncology thermotherapy, gene thermotherapy physiological therapy analysis; …
Medical Imaging Informatics DomainWhat is Medical Imaging Informatics (MII)?Medical Imaging Informatics (MII) == Radiology Informatics? Sub-specialty of radiology One of medical informatics disciplinesDomain Boundary Extension Medical imaging data management, processing, and displaying --- > medical image data mining?Demand: patient healthcare system andteleradiology/telemedicine Job market: 70,000 on demand, education challengesHawkeye Radiology Informatics (HRI)
MII DomainMedical Imaging Informatics (MII) ==Radiology Informatics (RI)? One of medical informatics Sub-specialty of radiologyDomain Boundary --- > medical image datamining?Technical driven: teleradiology/telemedicine Job market: 70,000 on demand, education challengesHawkeye Radiology Informatics (HRI)
MII Naming IdentityImaging Informatics in Medicine (IIM), alsoknown as Radiology Informatics (RI) orMedical Imaging Informatics (MII)A subspecialty of radiology that aims to improve Efficiency Accuracy Usability Reliability of medical imaging services within the healthcare enterprise
MII Naming IdentityThe fundamental issues in MII include Standards for image exchange Communication protocols, underlying computer data and knowledge representations Code Terminologies and vocabularies to be portable to electronic medical record, relation to digitalized healthcare Networked tele-radiology other IT issues (Kulikowski, 2002)
MII Naming IdentityMII is technically devoted to the study of howinformation about and contained within medicalimages is retrieved, analyzed, enhanced, andexchanged within radiology and throughout themedical enterprise
MII DomainRadiology is an inherently metadata-intensive and multi-technology-driven specialty of medicineRadiologists lead research and development in medical imaginginformaticsWith the proliferation of digitized images across the practice ofmedicine to include fields such as Cardiology Dermatology Surgery Gastroenterology Obstetrics, gynecology Pathology …
MII WorkforcesVarious industry playersVendors involved with medical imagingIT expertsOther biomedical informatics professionals
MII DomainThis domain interacts with broad disciplines: Biological science - includes bench sciences Biochemistry, microbiology Physiology and genetics Clinical services Practice of medicine Bedside research Outcomes and cost-effectiveness studies Public health policy Information science Acquisition, retrieval and manipulation of data Medical physics / engineering Use of equipment and technology for a medical purpose
MII DomainTraditionally, MII deals with PACS, imageprocessing and analysis.Now it moves towards the direction ofknowledge-based, “real” informaticsDeals with not only data formatting and theirintegrations but also with processing entities(Siniha, 2002)
MII DomainDue to its overlapping, it has many challenges Defining MII’s entities (Kulikowski, 1997) Classifying domain spaces Terminology and data integration (Umit, 2009) Interoperation in today’s in medical imaging informatics systems.In today’s digital radiology, the ontological standardsmust be encoded into an Entire-Patient-Entity (EPE)Collaborative team of radiologists and IT professionals(Geis, 2007)s
Cloud of Medical Informatics and Computational MedicineComputer Applications in Healthcare SystemKnowledge Management and Data Mining
Cloud of Medical Informatics Domains Bioinformatics (computational genomics, computational genetics) Medical imaging informatics Medical Sociology Computational medicine Health informatics Pathology informatics Biostatistics Nursing informatics
MII ConnectionsMII: medical image data and data processingRadiology Informatics: Frontier in cancerdiagnostics and image workflowProliferated applications: Oncology, cardiology, dermatology, surgery, gastroenterology, obstetrics, gynecology and pathology, and other medical fieldsStrong digital requirement and IT engagement
Interactions between MII and other medical informatics (Cloud) External Domain Connections Medical Imaging InformaticsOncology Radiology Pathology Cardiology Neurology Dermatology Obstetrics & gynecology Gastroenterology Surgery
Medical Imaging Informatics (MII) ScopeWhat is current scope of MII? A subspecialty of radiology that aims to improve medical imaging related discovery and technical services within the healthcare enterprise Accuracy (methodology) Efficiency (workflow) Usability (feasibility or applicability) Reliability (accessibility) Sustainability Cost/performance Its ultimate goal to improve health care systems
Medical Imaging Digital Radiology
Digitization In Medical Sciencesand Data Issue Digital Pathology Digital Radiology P e t a b y t e s E-Health Initiatives/Linkages Electronic Medical Record 40,000 BCEcave paintings bone tools 3500 writing 0 C.E. Digital Cardiology paper 105 1450 printing 1870 electricity, telephone transistor 1947 computing 1950 Late 1960s Internet 1993 The Web 1999 2003 Source: UC Berkeley, School of Information Management and Systems.
NM (128, 128)MRI (256, 256)CT (512, 512)DSA (1024, 1024)CR (2048, 2048)Mammogram (4096, 4096) From H. K Huang, 2004
Medical Imaging Informatics Infrastructure -- Crossing Medical Imaging and ITMedical Imaging Software ResourcesMedical Imaging Hardware Facility
Medical Imaging Informatics Infrastructure -- Crossing Medical Imaging and ITMedical Imaging Workforce Knowledge based computer-aided detection (diagnostics), CAD Clinic experience IT knowledge
Previous Paradigm: MII’s Data Oriented RoadmapStudy how medical images (within radiology andthroughout medical enterprise) are processed by Acquisition Archiving Retrieving/recovering Image Processing Analyzed Enhanced Visualized Data format conversion …
Current Paradigm: CrossingA multidiscipline and interdisciplinary Intersection with other fields: Medical science (radiology, internal medicine, neuroscience, …) Computer and information science Biomedical engineering Electrical engineering (signal and data processing) Biological and physiological sciences Medical physics …
Hospital Registration Order exam Waiting room Workflow Modality preparation radiologist review final report on RIS Exam operationFetch report to HIS Radiologist preview Paperwork film package
MII Challenges (1)PACS (Picture Archiving and CommunicationSystem) Different regional and industrial interpretation, configuration, and implementation Different interfaces and prototypes Different standardization DICOM, HL7, Other IT standards Different image digitalization of modalities Different scopes
MII Challenges (1)Technical Components Image acquisition and management technology Data visualization or image display Network and communications Computer application software
MII Challenges (1)PACS Technical Concerns Data Migration Back-up archive Fault-tolerance Integration with legacy systems Fast wide-area networks Security
PACS Distributed Computing Architecture Large scale (multiple module-based):Local networked Module 1 Module 2 Module 3 Distributed multiple- modules within multiple services units; but with single health organization
PACS Classification Super scale (enterprise-, cyberinfrastructure-, heterogeneous, distributed grid-based, cross organization, or even globally):High speednetwork Module 1 of site A Module 2 of site A Module 1 of Site B Module 2 of site B
MII Challenges (2)Lack generic MII ontology (Philological Issue) Systematic identification and classification of domain entities and existences, and entity relations (e.g., communication) No semantic languages for communications or workflows Loosely-defined terminology No linkage and leverage to knowledge, artificial intelligent (AI), decision making
Philology Ontology Wisdom Domain Cognitive Sciences Artificial IntelligenceEntity Relations Knowledge CAD Decision Making Information Science Information Management Metadata Metadata Metadata Data Data Data Data Data
MII Challenges (2)Ontological data model (terminologyclassification, entity definition, and relationsestablishing) (Methodology issue)Knowledge-drivenArtificial intelligence-drivenUnprecedented capacity for handling massivedataSystem integration and interoperation amongvarious hospital/clinic systems
MII Challenges (2)No standard protocols (Technical issues) To facilitate the interoperation and communication among globally-distributed MII resources To deploy concurrent hardware and software solutions To utilize cyber-enabled high-speed networks Short of education/training programs (Businessissue) To foster the next generation in digital health care systems.
MII Challenges (4)Software Development Computer-Aided Detection and Diagnosis (CAD) Computer-aided interventional radiology Metrics and computing performance Medical imaging facility and infrastructure development Fundamental research and development Medical Imaging Service Pack (MISP) Medical Imaging Informatics Knowledge Integration Toolkit (M2KIT)
Basic OntologyOntology is a major field in philosophymetaphysics. It is principal of entities (objects) and their ties.Ontology method Intuitive study of the fundamental properties, modes, and aspects of being, or of entities in general (Raul Corazzon, 2009). Ontology deals with the entities that are defined in philosophy as distinctions, or separate existences.
Basic OntologyOntologies Ontologically defined entities Used in system developmental models that better display communications and internal processing of communication medium such as medical data Study the nature of entity, reality, and basic categories of their relationshipsOntology‘s main theme Determine the categories of abstract being Deals with abstract objects that are considered as a set that contains subsets Development of external and internal connections (relationships)
Basic OntologyIn a technical perspective, a set of entities can bereferred to a special collection of abstract objects Shared characteristics and similar, intellectual activities or eventsEach object is depicted by Attributes Properties and characteristics Events Process of activities such as behaviors or functions
Significances of Basic OntologySuccessfully used in the foundation of semantics, especially inobject-oriented programming (OOP) languages in computer andinformation sciences Ontological concepts have been intensively employed into the semantics of object-oriented programming languages, such as C++/C# or Java Abstraction, Inheritance, and InterfaceCommon ontological approach to a cognitive and integrativesystem Needs to identify the extant entity and their categories with appropriate grouping or sorting schemesIn science, the ontology of scientific systems relates to manyprinciples and rules defined in theology, library science, andartificial intelligence
Basic Ontology: Attributes and ClassificationIn ontological approach, quintessential ontological concepts canbe applied to system design Universals and particulars Substance and accident Abstract and concrete objects Essence and existenceBefore one starts to develop a biomedical system architectureand design, he/she should consider the basic questions about how to create abstract classes of special entities, and identify the fundamentals and specific properties or attributes of the entities, as well as relations with other classes (sets).Common question in the design circle of healthcare informationsystems. DICOM standard is a perfect exam for this paradigm
Basic Ontology: RelationshipOnce the consideration of class is initiated, one shouldestablish system subject, object, and their relationshipswithin the system under consideration.This practical ontology paradigm has not only been applied to semantic descriptions in mathematics, computer science, and other physical sciences (Heller, 1990) but also been employed in social, religious, linguistics, and other communication related domains (John Symons, 2009).
Ontological EnvironmentsSuch philosophy of scientific domain is necessary toexplore the domain knowledge about being, as well asthe interactions with the subjects and objects of otherdomains of interdisciplinary and multidisciplinarysciencesThe interaction and connection with other domainsmust be seriously considered through ontologicalconcepts in terms of body and environment.It deals with either “is-” or “has” relationships.
Ontological EnvironmentsThe body takes some special actions in an environment Relied to a great degree on insights derived from scientific research into biomedical beings taking instinctive action in natural and artificial settings in biomedical sciences.The processes by which bodies related to environmentsbecame of great concernIdea of being itself became really difficult to define
Ontological EnvironmentsThe establishment of body and environment is basedon the ontological assumptions embedded within thesemantic descriptive medium tools for communications,such as semantic languages Context for communication: a horizon of unspoken background meanings.Because these assumptions both generate and areregenerated in our everyday interactions, the locus ofour way of being is the communicative event oflanguage in use, especially in biomedical research andmedical services.
Systematic Engineering RenovationSystem design’s paradigm, vision, and envisioning Subject-object relations Body and environmental relations Different ontological approaches (such as realism, empiricism, positivism, and post-modernism)Extremely important in today’s medical imaginginformatics, like PACS and radiology Information (RIS)or enterprise health information (eHIS) systems.
Systematic Engineering RenovationOntology-guided system approach becomes the superkey to provide strategic, scalable and sustainablesolutions to many integrated information-drivensystems.In the design of an enterprise health-care system, oneneeds to ensure that the system operates in an effective,efficient, consistent and reliable way, even process ofmedical data and tasks and their communication iscomplex.Such system design needs an ontological vision in termsof clear entity classification, subject/object relations,semantics in workflow and identity
Ontology for Computer and Information SciencesThe practicalities of achieving cohesion in aninformation-based society Is highly demanded Is paid attention to ontology, especially in information science.Computer Community Computer theory, artificial intelligence, formal and computational linguistics, biomedical informatics, conceptual modeling, knowledge engineering and information retrieva Realize that a solid foundation for their research calls for serious work in ontology, a general theory of the types of entities and relations that make up their respective domains of inquiry
Ontology for Computer and Information SciencesAttention is now being focused on the content ofinformation rather than on just the formats andlanguages used to represent information.The clearest example of this development is providedby the numerous initiatives powdered by Semantic Web(W3C).Need for integrating research in these different fieldsarises, so does the realization that strong principles forbuilding well-founded ontologies might providesignificant advantages over ad hoc, case-based solutions.
Ontology for Computer and Information SciencesTools of formal ontology address precisely theseneeds, but a real effort is required in order toapply such philosophical tools to the domain ofinformation systemsReciprocally, research in the informationsciences raises specific ontological questionswhich call for further philosophicalinvestigations.
Ontology for Computer and Information SciencesMany efforts have been made in establishingcommunities in theoretical and practicalontology for information science, as well as inother domain of ontologies.
Ontology for Computer and Information SciencesFormal Ontology in Information Science (FOIS) holdssequential conferences on many fundamental andpractical issues of theorems and concrete applications,including Concepts, theorems, and usages: kinds of entity particulars vs. universals continuants vs. occurrence abstract vs. concrete dependent vs. independent natural vs. artificial;
Ontology for Computer and Information SciencesFormal relations vagueness and granularity identity and change formal comparison among domain ontologies ontology of physical reality ontology of mental reality ontology of social reality ontology of the information society ontology and natural and artificial language semantics ontology and cognition ontology and medical domains, semiotics
Ontology for Computer and Information SciencesMethodologies and Applications Top-level vs. application ontologies Role of reference ontologies Ontology integration and alignment Ontology-driven information systems design requirements engineering, knowledge engineering Knowledge management and organization Knowledge representation Qualitative modeling, computational lexica Terminology information retrieval Question-answering, semantic Web Web services; cloud and Grid computing, domain-specific ontologies (linguistics, geography, law, library science, biomedical science, e- business, enterprise integration).
Promises of Ontology for Biomedical Informatics Information science forwards to the gateway of knowledge-driven, biomedical data based scientific and health care discoveries, and decision making mechanisms with highly intelligent communication protocols. Such roadmap strongly requires large-scale system’s integration and high interoperability, and highly- efficient communication workflow of data and tasks.
Promises of Ontology for Biomedical Informatics The methodology and conceptual rigor of a philosophically inspired formal ontology can bring significant benefits in the development and maintenance of application ontologies (Flett et al, 2003). Such promise can be inspirited by many testaments in the collaboration between biomedical methodologies and terminologies with semantic languages and computing technologies for supporting natural language processing, especially in the medical fields. Barry Smith from the Institute for Formal Ontology and Medical Information Science (IFOMIS) clearly addressed the theoretical foundations of ontology (Barry Smith, 2009).
Medical Industrials NeedsTo transform a large terminology-basedontology into one special domain to supportpractical applications becomes an urgentdemand in healthcare industrials.For example, a general procedure has been theimplementation of a meta-ontological definitionspace in which the definitions of all the conceptsand relations can be defined in LinkBase(Montayne and Flanagan, 2003).
Natural Language Processing (NLP) Language and Computing (L&C)’s advanced TeSSI® technology suite is developed to meet the needs of a broad range of industries: healthcare providers, pharmaceutical and R&D companies, Government, HIT vendors and software developers. L&C offers advanced solutions to those sectors where our tools and technology can offer a significant benefit in natural language processing (NLP) solution, terminology/ontology and knowledge management (content management, search and retrieval, information extraction, text mining, Semantic Web). Its solutions allow L&C customers and partners to leverage NLP technology to address a variety of healthcare and business needs.
Medical Education NeedsIn education, many schools, including medical schools,start to teach or provide workshops in ontologicalapproaches.Such education programs commonly introduce a newapproach to the development of ontologies applicableto the medical domain.The programs highlight the need for a rich reference onpractical ontology for healthcare phenomena andservices, and consider issues or challenges arising in themedical applications of such a theoretically groundedontology in improving working applications in thedomain of electronic patient records (EPR).
Medical Education NeedsMany institutions gradually begin to crystallize inthe domain of information systems ontology. Some education programs focus primarily on the realism or adequacy of an underlying ontological theory, leaving for others the task of transforming this reference ontology into biomedical working applications. They present recent developments in the field of realist ontology, focusing especially on biomedical information science.
Medical Education NeedsOthers focus primarily on the construction of workingapplications at the expense of ontological realism to practicalbiomedical informatics, with an emphasis on the applicability ofmedical natural language understanding applications to detectinconsistencies and medical errors in patient reports Specific systems in healthcare, and to foster new workforce in learning advanced technologies or tools High dimensional ontologies (substances, qualities, functions and processes; space and time; niches, contexts, environments) unifying ontologies of medicine at different levels of granularity, problematic issues: absences, norms, reference to individual patients in clinical histories, conflicting clinical guidelines, computational tractability vs. ontological adequacy.
Medical Education NeedsHowever, there still remains many challengeissues, fundamentally. For example, in biomedical terminology or ontology development, one should consider how to identify relevant shortcomings in existing systems and how to properly describe niches/contexts of healthcare.
Medical Education NeedsFuture electronic patient record (EPR) systemdevelopers must integrate their special domainapplications to large-scale systems How to cooperatively and collaboratively develop general robotic, dynamic, and interoperable standards of data ontology model, repository, standard expression and formation, and utility tools becomes a common task. Current biomedical ontology research and development promoted and supported by NIH programs and NCI’s and caBiG in USA built significant foundations for future development
caBIGA collection of imaging informatics tools developed under theumbrella of the National Cancer Institutes caBIG (cancerBiomedical Informatics Grid) demonstrates the potential forcollecting, analyzing, integrating, and disseminating informationassociated with cancer research and care.The caBIG is an information network enabling all constituenciesin the cancer community to share data and knowledge toaccelerate the discovery of new diagnostics and therapeutics, andto improve patient outcomes.The caBIG imaging workspace was created as a nationalmultidisciplinary expert advisory board for the identification andprioritization of imaging informatics projects.
Biomedical Ontology Research DevelopmentOver the past decades, ontologies have become keycomponents of information systems (Gruber, 1993;Guarino, N., 1998)Various applications Natural language processing (NLP) (Nirenburg and Raskin, 2004) Software engineering (Calero, et. al 2006) Knowledge management in the semantic Web (Kashyap, 2008).The use of ontologies also spans many domainsincluding biomedicine, geography and e-commerce.
Biomedical Ontology Research DevelopmentIn the biomedical domain, ontologies play an importantrole in research (Blake and Bult, 2006), healthcare(Garde et al., 2007) and translational biomedicine(Ruttenberg, 2008), supporting tasks including knowledge management, data integration and decision support (Bodenreider, 2008).A large number of biomedical ontologies have beencreated by individual researchers, academic consortia,institutions and companies.
Biomedical Ontology Research DevelopmentMost ontologies are created for a given purpose (e.g., toannotate gene products) and independently of existingontologies.As a consequence, although ontologies are ideallycreated for sharing and reusing knowledge (Musen,1992), its impact is still limited to small groups in aspecial field.The abundance of ontologies should account for greatresources to biomedical researchers in their biomedicalapplications.
Biomedical Ontology Research DevelopmentIn practice, however, there is no comprehensiveregistry of biomedical ontologiesFew existing collections of ontologies do notprovide enough information about theontologies to support effective discoverability.
Biomedical Ontology Research DevelopmentUMLS Unified Medical Language System (UMLS) (Bodenreider, 2004) Developed at the National Library of Medicine, which integrates many biomedical terminologies and ontologies.
Biomedical Ontology Research DevelopmentBioPortal National Center for Biomedical Ontologies (NCBO), funded by NIH Roadmap Web-based application providing access to a large number of ontologies in the biomedical domains (Bodenreider, O., 2004). NCBO’s BioPortal “defines relationships among those ontologies and between the ontologies and online data resources” and supports “community-based participation in the evaluation and evolution of ontology content” (Musen, 2008). BioPortal enables biomedical ontology users to discover, visualize, download and evaluate existing resources and their interrelations. BioPortal will host the ontologies from the MetaOntology issue and their associated metadata Analogously, the Ontology Lookup Service (Cote, 2008) developed by the European Bioinformatics Institute supports queries against ontologies from the Open Biomedical Ontology (OBO) family.
Biomedical Ontology Research DevelopmentResources pros and cons Offer online lookup services, i.e., support the discovery of individual entities through their names and identifiers. Although these ontologies are generally useful to researchers, none of these resources is comprehensive enough The interfaces to ontologies are no substitute for a rich description of their content and characteristics.
Ontology for Medical Imaging InformaticsAlthough the XML technology has been successful for softwarein a heterogeneous environment to establish consistency of ITdata format and expression, currently, there is no easy way tomap the knowledge contained in different domain sets.This is due to the different domain structure, culture, andexpression in languages. For example, in radiology community, a radiologist uses medical imaging in their daily diagnostics.A standard terminology and anthologies are extremely importantin order to improve the clarity of report and efficientcommunication; and to enhance their clinical and healthcareservices.
Ontology for Medical Imaging InformaticsRadLext a Lexicon for Uniform Indexing and Retrieval of Radiology Information Resources RadLext is supported both by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and by the cancer Biomedical Informatics Grid (caBIG) project.One of the challenges is how to better translatethe RadLext terminology to an ontologyrepresentation in MII systems
Ontology for Medical Imaging InformaticsRubin (2008) recently reports their successfultranslation of terminology form the RadLextformat to the one of Protégé, an open sourceontology management tool.There still needs to establish a standard,ontology-based knowledge bank of medicalimaging informatics using ontologicalexpressions, rather than redundantrepresentations and translations
Ontology for Medical Imaging InformaticsIt is of urgent need to develop an efficientmechanism allows to map the terminology from one domain set, say radiology, to another domain set, say, neuro- imaging when they deal with brain MRI data.The data ontology model becomes the firstphase to establish integrated ontological domainmodels.
Ontology for Medical Imaging InformaticsIn the DICOM ontology, among other projects,cerates a single common reference informationmodel for all caBIG projects that need to referto imaging and other DICOM-relatedinformation in their individual information.Most of the current MII tools in PACS or RISsystems are web-application based software fordiagnostics or reporting (Corey et al, 2007).
Ontology for Medical Imaging InformaticsDue to the recently-developed Web-services for onlineimage reading and reporting in tele-radiology, theWSDL, Semantic Web, Web Ontology language (OWL),XML and other W3C protocols, with Hl7 and DICOMstandards, are the fundamental components inontological approach in MII.The ontologically-defined service objects should beapplicable to RIS and HIS systems and associated tools.They should have high compatibility and portability,and should appropriately be used in EHP systems(Smith and Ceusters, 2006).
Ontology for Medical Imaging InformaticsThe National Center of Biomedical Ontology(NCBO) provides many fundamental tools andontology templates allow researchers and investigators to conduct a new ontology set under the roadmap of NIH, compatible with caGrid terminology server in caBIG.
MIIO ProjectWe initiated a “Medical Imaging InformaticsOntology”, or MIIO, which maps two domains:medical imaging and information technology.
MIIO ProjectThe project collects 3800 technical terms (entities)MIIO project team starts to define each of the terms inontology expression Domain space definition, domain entity definition, space belongs (is relationship), embedding attributes (has relationships), inner relationships among the other entities within the domain space, outer relationships to communicate with other entities defined in other domain space, entity attributes, characteristics, feature, and properties, entity information acquisition and achieving, and retrieving methods, entity (object) declaration and construction, and entity abstraction and inheritance, etc.
MIIO ProjectThe domain space currently only covers medicalimaging and information technology.MIIO’s infrastructure is designed to be highlyscalable to extend to other domain space andportable and compatible to other standards inontology expressions.
MIIO Project Strategic Task Actions Multiple expressions in multiple ontological formats or expressions, mainly in OWL, OBO, UMLS RRF, Progete, and Progete-OWL respectively. A dynamic search engine For the uses of special domain ontologies By a Java search code through Google search engine. Statistical data mining The entities were found by decoding the words using string token and by a statistical analysis to see the appearing with each articles or online pages HPC-enabled
MIIO-TK ModulesThe MIIO-TK has several components ormodules File module -- allows users to open file and project Search Module –a search engine Definition module --- for input, modify, and express module Format conversion module --- allows users to convert one expression format to another Visualization module --- allows users to visualize different formatted enmity expression.
Spotlight of MIIO ToolKit
Lab MissionEstablishment of a nationally and globally-recognized research lab in medical imaginginformatics or radiology informatics
Short Term Action TasksLearning any subjects and shaping knowledgeDevelop infrastructure of unprecedented computingfacility in medical imaging informaticsCollaborating with enterprise IT and health careindustrialsWorking with external and internal professionalsSeeking for fundsDevelop software solutions for future health caresystemsAttract more people including you.
Long-Term GoalComputation (future projects) Infrastructure and algorithm developments for data mining in medical image Artificial Intelligence in medical imaging Large-scale image processing and associated modeling and simulations Digitalization of human body (mass-phantom system) Computational radiology System radiology
MIHI Lab ProjectsMedical Imaging & Radiology Informatics (MIRI) Hawkeye Radiology Informatics (HRI) http://www.uiowa.edu/~hri/ Radiology Informatics Domain Ontology (RIDO) Medical Imaging Informatics Ontology (MIIO) Medical Imaging Informatics Terminology (MIIT) Cyberinfrastructure-enabled Radiology Informatics (CIRI) Medical Imaging Information System (MIIS) http://www.uiowa.edu/mihpclab/projects_miis.html Radiology Informatics Education and Training (RIET) http://www.uiowa.edu/~hri/education.html
ProjectsParallel Computing in Medical Imaging (PCMI) http://www.uiowa.edu/mihpclab/projects_pcmi.html Parallelism of Medical Imaging Processing CT Reconstruction Segregation Registration Texturing and classification Enhancement Image compression Image data mining …
ProjectsModeling Biotransport in Biophysical System (MBBS) http://www.uiowa.edu/mihpclab/projects_mbbs.html Nanothermotheropy (nanoHyperthmia) http://www.uiowa.edu/mihpclab/projects_nmni.html Tumor growth and dynamics (computational oncology)Optical Imaging Tomography and Applications (OITA) http://www.uiowa.edu/mihpclab/projects_oita.html
ProjectsStereological Analysis and Tumor VolumeMetrics (SATVM) voluMeasure Software Development Project (RSNA05) The Effect of the Shape and Orientation of a Mass on the Accuracy Estimating Its Size Using RECIST (RSNA09) Tumor volume measurement in MRI breast imaging http://www.uiowa.edu/mihpclab/projects_isca.htmlStereotactic Atlas for the Anatomic Topology(SAAT) http://www.uiowa.edu/mihpclab/projects_saat.htmlCouple Diffusions for Image Enhancement (DDIE) http://www.uiowa.edu/mihpclab/projects_cdie.html
ProjectsKnowledge-based CAD for Breast Imaging(KCBI) architectural distortion calcification Deformation http://www.uiowa.edu/mihpclab/projects_kcbi.htmlNew projects Tomosynthesis and Molecular Breast Imaging US Medical Imaging 3D Volume Rendering
Sponsorships and CollaborationsCurrent sponsors NIH (NIBIB-medical imaging) NSF (CISE-computer information science and engineering) Intel (High performance computing) Microsoft (Window computing server, technical specification and software development)Collaborators: Siemens (Medical modality and software resources) IBM (Cell/BE/OpenCL-based medical imaging) Navida (GPU/CUDA-based medical imaging) Mayo Clinic (Cloud computing-based medical imaging)
Institutional CollaborationsUI-Medical School – SJTU-Medical School