Automated Semantic Indexing of Figure Captions to Improve Radiology Image Retrieval
- aDivision of Informatics, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI
- bDepartment of Radiology and Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA
- Correspondence: Division of Informatics, Department of Radiology, Medical College of Wisconsin, 9200 W. Wisconsin Ave., Milwaukee, WI 53226; e-mail: <kahn{at}mcw.edu>
- Received 29 July 2008
- Accepted 9 February 2009
Abstract
Objective We explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals.
Design The MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval.
Measurements Precision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine.
Results Estimated precision was 0.897 (95% confidence interval, 0.857–0.937). Estimated recall was 0.930 (95% confidence interval, 0.838–1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone.
Conclusion Concept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval.
Footnotes
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This study was supported in part by the American Roentgen Ray Society. The work also was supported in part by the National Center for Biomedical Ontology under roadmap-initiative grant U54 HG004028 from the NIH.
