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Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images
BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperativ...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5370472/ https://www.ncbi.nlm.nih.gov/pubmed/28351368 http://dx.doi.org/10.1186/s12938-017-0323-1 |
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author | Lu, Xuesong Yang, Rongqian Xie, Qinlan Ou, Shanxing Zha, Yunfei Wang, Defeng |
author_facet | Lu, Xuesong Yang, Rongqian Xie, Qinlan Ou, Shanxing Zha, Yunfei Wang, Defeng |
author_sort | Lu, Xuesong |
collection | PubMed |
description | BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. METHODS: In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. RESULTS: We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text] ) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text] ) for right atrium. CONCLUSIONS: The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart. |
format | Online Article Text |
id | pubmed-5370472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53704722017-03-30 Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images Lu, Xuesong Yang, Rongqian Xie, Qinlan Ou, Shanxing Zha, Yunfei Wang, Defeng Biomed Eng Online Research BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. METHODS: In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. RESULTS: We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text] ) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text] ) for right atrium. CONCLUSIONS: The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart. BioMed Central 2017-03-28 /pmc/articles/PMC5370472/ /pubmed/28351368 http://dx.doi.org/10.1186/s12938-017-0323-1 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Lu, Xuesong Yang, Rongqian Xie, Qinlan Ou, Shanxing Zha, Yunfei Wang, Defeng Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images |
title | Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images |
title_full | Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images |
title_fullStr | Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images |
title_full_unstemmed | Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images |
title_short | Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images |
title_sort | nonrigid registration with corresponding points constraint for automatic segmentation of cardiac dsct images |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5370472/ https://www.ncbi.nlm.nih.gov/pubmed/28351368 http://dx.doi.org/10.1186/s12938-017-0323-1 |
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