| A discriminative-generative model for detecting intravenous contrast in CT images. | |
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MedLine Citation:
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PMID: 22003683 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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This paper presents an algorithm for the automatic detection of intravenous contrast in CT scans. This is useful e.g. for quality control, given the unreliability of the existing DICOM contrast metadata. The algorithm is based on a hybrid discriminative-generative probabilistic model. A discriminative detector localizes enhancing regions of interest in the scan. Then a generative classifier optimally fuses evidence gathered from those regions into an efficient, probabilistic prediction. The main contribution is in the generative part. It assigns optimal weights to the detected organs based on their learned degree of enhancement under contrast material. The model is robust with respect to missing organs, patients geometry, pathology and settings. Validation is performed on a database of 400 highly variable patients CT scans. Results indicate detection accuracy greater than 91% at approximately 1 second per scan. |
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Authors:
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Antonio Criminisi; Krishna Juluru; Sayan Pathak |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Volume: 14 ISSN: - ISO Abbreviation: Med Image Comput Comput Assist Interv Publication Date: 2011 |
Date Detail:
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Created Date: 2011-10-18 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101249582 Medline TA: Med Image Comput Comput Assist Interv Country: Germany |
Other Details:
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Languages: eng Pagination: 49-57 Citation Subset: IM |
Affiliation:
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Microsoft Research Ltd., CB3 0FB, Cambridge, UK. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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