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Annotation and Image Markup : Take AIM at Images!

Annotation and Image Markup : Take AIM at Images!. Principal Investigators:. David S. Channin M.D. Associate Professor of Radiology Chief, Imaging Informatics Northwestern University Feinberg School of Medicine Department of Radiology. Daniel Rubin M.D.

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Annotation and Image Markup : Take AIM at Images!

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  1. Annotation and Image Markup:Take AIM at Images! Principal Investigators: David S. Channin M.D. Associate Professor of Radiology Chief, Imaging Informatics Northwestern University Feinberg School of Medicine Department of Radiology Daniel Rubin M.D. Clinical Assistant Professor of Radiology Research Scientist Stanford University Department of Medical Informatics Dr. Pat Mongkolwat  Vladimir Kleper  Skip Talbot Kaustubh Supekar

  2. What is an AIM? Annotations are explanatory or descriptive information, generated by humans or machines, directly related to the content of a referenced image or images Image annotations let us capture information about the meaning of pixel information in images such that similar meaning in other images can be found and used. An image markup is the graphical symbols associated with an image and optionally with one or more annotations of that same image.

  3. An Image

  4. An Image and an Image Markup

  5. An Image, an Image Markup and an Annotation The pixel at the tip of the arrow [coordinates (x,y)] inthis image [DICOM: 1.2.814.234543.23243]represents the Ascending Thoracic Aorta[SNOMED:A3310657]

  6. An Image, an Image Markup and an Annotationin AIM Schema The pixel at the tip of the arrow [coordinates (x,y)] inthis image [DICOM: 1.2.814.234543.23243]represents the Ascending Thoracic Aorta[SNOMED:A3310657]

  7. What is the solution? The caBIG AIM Project • An ontology of image annotations • An ontology of image markups • An ontology defines concepts in a domain and the relationships between those concepts • Use of controlled terminologies • EVS, RadLex, SNOMED, LOINC, UCUM • A set of translatable, standards-based representations

  8. The Deliverables • Two ontologies • used to develop information models • A DICOM S/R AIM Object and a caBIG object model of the DICOM S/R object • HL7 CDA (xml) encoding of DICOM S/R object • A software application to validate and transcode AIM annotations (ANIVATR) • An AIM toolkit to create and render AIM annotations and markups • Integrated into the eXtensible Imaging Platform

  9. The Real Deliverables • Two One ontology of both annotation and markup • A (someday silver compliant) UML model of AIM • Software to instantiate AIM XML • Software to generate DICOM S/R AIM Object from AIM XML • Software to generate HL7 CDA (xml) encoding of DICOM S/R object from AIM XML via HL7 CDA XML Schema • A software application XIP Builder ScheneGrph and XIP modules to validate and transcode AIM annotations (ANIVATR) • An AIM toolkit library to create and render AIM annotations and markups • (Integrated into the eXtensible Imaging Platform)

  10. The Deliverables • 1Q 2007 • Project Management Plan • Communication Plan • Draft IOSA Document • Draft Reconciliation Document • Final IOSA Document • Final Reconciliation Document • Quarterly Progress Report • 2Q 2007 • Draft Mechanism for Free Text • Draft Mechanism for Arbitrary Calculation • Final Mechanism for Free Text • Final Mechanism for Arbitrary Calculation • Sample Instances • Quarterly Status Report • 3Q 2007 • Software Demonstration • Import, validate, create, transcode, etc. • Quarterly Status Report • 4Q 2007 • RSNA 2007 Demonstration • Quarterly Status Report • Lessons Learned

  11. API • Give me the XML schema for an XYZ AIM • (Driven by Protégé) • Constrain vocabulary choices • Set the … • Get the … • Save As.. • DICOM S/R • XML/CDA

  12. Summary • Image annotations and markups are critical to “tagging” content in medical images • Such that images containing similar content can be identified • The AIM project will deliver an information model and encoding standards for the structure and content of image annotations • AIM annotations will be critical components of future image based research.

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