| Classifier selection strategies for label fusion using large atlas databases. | |
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MedLine Citation:
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PMID: 18051099 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Structural segmentations of brain MRI can be generated by propagating manually labelled atlas images from a repository to a query subject and combining them. This method has been shown to be robust, consistent and increasingly accurate with increasing numbers of classifiers. It outperforms standard atlas-based segmentation but suffers, however, from problems of scale when the number of atlases is large. For a large repository and a particular query subject, using a selection strategy to identify good classifiers is one way to address problems of scale. This work presents and compares different classifier selection strategies which are applied to a group of 275 subjects with manually labelled brain MR images. We approximate an upper limit for the accuracy or overlap that can be achieved for a particular structure in a given subject and compare this with the accuracy obtained using classifier selection. The accuracy of different classifier selection strategies are also rated against the distribution of overlaps generated by random groups of classifiers. |
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Authors:
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P Aljabar; R Heckemann; A Hammers; J V Hajnal; D Rueckert |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
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: 10 ISSN: - ISO Abbreviation: Med Image Comput Comput Assist Interv Publication Date: 2007 |
Date Detail:
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Created Date: 2007-12-04 Completed Date: 2008-01-03 Revised Date: 2009-12-11 |
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: 523-31 Citation Subset: IM |
Affiliation:
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Department of Computing, Imperial College London, UK. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence* Brain / anatomy & histology* Databases, Factual* Humans Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Imaging, Three-Dimensional / methods Information Storage and Retrieval / methods Magnetic Resonance Imaging / methods* Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity Subtraction Technique* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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