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Proceedings Paper

Semantic annotation of medical images
Author(s): Sascha Seifert; Michael Kelm; Manuel Moeller; Saikat Mukherjee; Alexander Cavallaro; Martin Huber; Dorin Comaniciu
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Paper Abstract

Diagnosis and treatment planning for patients can be significantly improved by comparing with clinical images of other patients with similar anatomical and pathological characteristics. This requires the images to be annotated using common vocabulary from clinical ontologies. Current approaches to such annotation are typically manual, consuming extensive clinician time, and cannot be scaled to large amounts of imaging data in hospitals. On the other hand, automated image analysis while being very scalable do not leverage standardized semantics and thus cannot be used across specific applications. In our work, we describe an automated and context-sensitive workflow based on an image parsing system complemented by an ontology-based context-sensitive annotation tool. An unique characteristic of our framework is that it brings together the diverse paradigms of machine learning based image analysis and ontology based modeling for accurate and scalable semantic image annotation.

Paper Details

Date Published: 11 March 2010
PDF: 8 pages
Proc. SPIE 7628, Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 762808 (11 March 2010); doi: 10.1117/12.844207
Show Author Affiliations
Sascha Seifert, Siemens Corp. (Germany)
Michael Kelm, Siemens Corp. (Germany)
Manuel Moeller, German Research Ctr. for Artificial Intelligence (Germany)
Saikat Mukherjee, Siemens Corp. (United States)
Alexander Cavallaro, Universitätsklinikum Erlangen (Germany)
Martin Huber, Siemens Corp. (Germany)
Dorin Comaniciu, Siemens Corp. (United States)

Published in SPIE Proceedings Vol. 7628:
Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications
Brent J. Liu; William W. Boonn, Editor(s)

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