Semantic reasoning with image annotations for tumor assessment.
Identifying, tracking and reasoning about tumor lesions is a central task in cancer research and clinical practice that could potentially be automated. However, information about tumor lesions in imaging studies is not easily accessed by machines for automated reasoning. The Annotation and Image Markup (AIM) information model recently developed for the cancer Biomedical Informatics Grid provides a method for encoding the semantic information related to imaging findings, enabling their storage and transfer. However, it is currently not possible to apply automated reasoning methods to image information encoded in AIM. We have developed a methodology and a suite of tools for transforming AIM image annotations into OWL, and an ontology for reasoning with the resulting image annotations for tumor lesion assessment. Our methods enable automated inference of semantic information about cancer lesions in images.
Levy
Mia A
MA
Stanford University, Stanford, CA, USA.
O'Connor
Martin J
MJ
Rubin
Daniel L
DL
D002986:Clinical Trials as Topic; D003952:Diagnostic Imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009323:Natural Language Processing; D009369:Neoplasms / Q000175:diagnosis*; D011381:Programming Languages; D012660:Semantics