title: [Automatic segmentation and annotation in radiology].
abstract: The technical progress and broader indications for cross-sectional imaging continuously increase the number of radiological images to be assessed. However, as the amount of image information and available resources (radiologists) do not increase at the same pace and the standards of radiological interpretation and reporting remain consistently high, radiologists have to rely on computer-based support systems. Novel semantic technologies and software relying on structured ontological knowledge are able to "understand" text and image information and interconnect both. This allows complex database queries with both the input of text and image information to be accomplished. Furthermore, semantic software in combination with automatic detection and segmentation of organs and body regions facilitates personalized supportive information in topographical accordance and generates additional information, such as organ volumes. These technologies promise improvements in workflow; however, great efforts and close cooperation between developers and users still lie ahead.
authors: Radiologisches Institut, Universitätsklinikum Erlangen, Maximiliansplatz 1, 91054, Erlangen, Deutschland.
authors: P
authors: P
authors: Dankerl
authors: A
authors: A
authors: Cavallaro
authors: M
authors: M
authors: Uder
authors: M
authors: M
authors: Hammon
mesh_terms: D001185:Artificial Intelligence; D003660:Decision Support Systems, Management; D004282:Documentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems; D009626:Terminology as Topic