NIH | National Cancer Institute | NCI Wiki  

Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

  • Anatomic Entity
    Anatomic Entity CharacteristicImaging ObservationImaging Observation CharacteristicInferenceCalculation Characteristic, Imaging Observation, Imaging Observation Characteristic, Inference and Calculation 

Anatomic entity may have one or more anatomic entity characteristics. An anatomic entity characteristic contains every attribute that a component has. It has “annotator confidence” that allows a user to enter a level of user’s confidence answering the question in terms of a percentage. A characteristic may have a quantification value.

  • Quantile

...

  • Interval
  • Scale
  • QuantileNon-quantifiable
  • NumericalAnatomic Entity CharacteristicQuantificationIntervalScaleQuantileNon-quantifiableNumerical 

Imaging observation may have one or more imaging observation characteristics. An imaging observation characteristic contains every attribute that a component has. It has “annotator confidence” that allows a user to enter a level of user’s confidence answering the question in terms of a percentage. A characteristic may have a of the below quantifications and non-quantifiable.  

  • Quantile
  • Interval
  • Scale
  • QuantileNon-quantifiable
  • NumericalImaging Observation CharacteristicQuantificationIntervalScaleQuantileNon-quantifiableNumerical 

AIM Template Creation Process 

...

A glioblastoma multiform template was designed to leverage a controlled terminology for describing the 26 subjective MR features of human gliomas that was devised based upon prior work (VASARI project). The features comprehensively describe the morphology of brain tumors visualized on contrast-enhanced MRI. They can be captured as caBIGTM AIM formats, i.e. XML or DICOM SR, on the TCGA Radiology research workstation. MR images of TCGA gliomas are located at the National Biomedical Imaging Archive (NBIA). 


Summary 

Image annotations and markups are critical to “tagging” content in medical images. AIM annotations will be critical components of future image based research. The AIM project delivers an information model, encoding standards for the structure and content of image annotations, a web application used to create a controlled set of questions and answers that are captured as coded terms for a computer program to process.