Patient metadata-constrained shape models for cardiac image segmentation

Show simple item record Pereañez, M en Lekadir, K en Albà, X en Medrano Gracia, Pau en Young, Alistair en Frangi, A en 2018-10-08T02:55:03Z en 2016-01-01 en
dc.identifier.isbn 9783319287119 en
dc.identifier.issn 0302-9743 en
dc.identifier.uri en
dc.description.abstract © Springer International Publishing Switzerland 2016. Patient metadata such as demographic information and cardio vascular disease (CVD) indicators are valuable data readily available in clinical practice. This information can be used to inform the construction of customized statistical shape models fitting the patient’s unique characteristics. However, to the best of our knowledge, no studies have reported using these types of metadata in the construction of shape models for image segmentation. In this paper, we propose the use of a conditional model framework to include these patient metadata in the construction of a personalized shape model and evaluate its effect on image segmentation. Our validation on a dataset of 250 asymptomatic cardiac MR images shows an average segmentation improvement of 7% and in some cases up to 30% over a conventional PCA-based framework. These results show the potential of our technique for improved shape analysis. en
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.title Patient metadata-constrained shape models for cardiac image segmentation en
dc.type Conference Item en
dc.identifier.doi 10.1007/978-3-319-28712-6_11 en
pubs.begin-page 98 en
pubs.volume 9534 en
dc.rights.holder Copyright: The author en
pubs.end-page 107 en
pubs.publication-status Published en
dc.rights.accessrights en
pubs.elements-id 522557 en Bioengineering Institute en ABI Associates en Medical and Health Sciences en Medical Sciences en Anatomy and Medical Imaging en
dc.identifier.eissn 1611-3349 en

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