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A model-guided method for improving coronary artery tree extractions from CCTA images

PURPOSE: Automatically extracted coronary artery trees (CATs) from coronary computed tomography angiography images could contain incorrect extractions which require manual corrections before they can be used in clinical practice. A model-guided method for improving the extracted CAT is described to...

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Autores principales: Cao, Qing, Broersen, Alexander, Kitslaar, Pieter H., Lelieveldt, Boudewijn P. F., Dijkstra, Jouke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373332/
https://www.ncbi.nlm.nih.gov/pubmed/30488262
http://dx.doi.org/10.1007/s11548-018-1891-7
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author Cao, Qing
Broersen, Alexander
Kitslaar, Pieter H.
Lelieveldt, Boudewijn P. F.
Dijkstra, Jouke
author_facet Cao, Qing
Broersen, Alexander
Kitslaar, Pieter H.
Lelieveldt, Boudewijn P. F.
Dijkstra, Jouke
author_sort Cao, Qing
collection PubMed
description PURPOSE: Automatically extracted coronary artery trees (CATs) from coronary computed tomography angiography images could contain incorrect extractions which require manual corrections before they can be used in clinical practice. A model-guided method for improving the extracted CAT is described to automatically detect potential incorrect extractions and improve them. METHODS: The proposed method is a coarse-to-fine approach. A coarse improvement is first applied on all vessels in the extracted CAT, and then a fine improvement is applied only on vessels with higher clinical significance. Based upon a decision tree, the proposed method automatically and iteratively performs improvement operations for the entire extracted CAT until it meets the stop criteria. The improvement in the extraction quality obtained by the proposed method is measured using a scoring system. 18 datasets were used to determine optimal values for the parameters involved in the model-guided method and 122 datasets were used for evaluation. RESULTS: Compared to the initial automatic extractions, the proposed method improves the CATs for 122 datasets from an average quality score of 87 ± 6 to 93 ± 4. The developed method is able to run within 2 min on a typical workstation. The difference in extraction quality after automatic improvement is negatively correlated with the initial extraction quality (R = − 0.694, P < 0.001). CONCLUSION: Without deteriorating the initially extracted CATs, the presented method automatically detects incorrect extractions and improves the CATs to an average quality score of 93 guided by anatomical statistical models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11548-018-1891-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-63733322019-03-01 A model-guided method for improving coronary artery tree extractions from CCTA images Cao, Qing Broersen, Alexander Kitslaar, Pieter H. Lelieveldt, Boudewijn P. F. Dijkstra, Jouke Int J Comput Assist Radiol Surg Original Article PURPOSE: Automatically extracted coronary artery trees (CATs) from coronary computed tomography angiography images could contain incorrect extractions which require manual corrections before they can be used in clinical practice. A model-guided method for improving the extracted CAT is described to automatically detect potential incorrect extractions and improve them. METHODS: The proposed method is a coarse-to-fine approach. A coarse improvement is first applied on all vessels in the extracted CAT, and then a fine improvement is applied only on vessels with higher clinical significance. Based upon a decision tree, the proposed method automatically and iteratively performs improvement operations for the entire extracted CAT until it meets the stop criteria. The improvement in the extraction quality obtained by the proposed method is measured using a scoring system. 18 datasets were used to determine optimal values for the parameters involved in the model-guided method and 122 datasets were used for evaluation. RESULTS: Compared to the initial automatic extractions, the proposed method improves the CATs for 122 datasets from an average quality score of 87 ± 6 to 93 ± 4. The developed method is able to run within 2 min on a typical workstation. The difference in extraction quality after automatic improvement is negatively correlated with the initial extraction quality (R = − 0.694, P < 0.001). CONCLUSION: Without deteriorating the initially extracted CATs, the presented method automatically detects incorrect extractions and improves the CATs to an average quality score of 93 guided by anatomical statistical models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11548-018-1891-7) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-11-28 2019 /pmc/articles/PMC6373332/ /pubmed/30488262 http://dx.doi.org/10.1007/s11548-018-1891-7 Text en © The Author(s) 2018 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.
spellingShingle Original Article
Cao, Qing
Broersen, Alexander
Kitslaar, Pieter H.
Lelieveldt, Boudewijn P. F.
Dijkstra, Jouke
A model-guided method for improving coronary artery tree extractions from CCTA images
title A model-guided method for improving coronary artery tree extractions from CCTA images
title_full A model-guided method for improving coronary artery tree extractions from CCTA images
title_fullStr A model-guided method for improving coronary artery tree extractions from CCTA images
title_full_unstemmed A model-guided method for improving coronary artery tree extractions from CCTA images
title_short A model-guided method for improving coronary artery tree extractions from CCTA images
title_sort model-guided method for improving coronary artery tree extractions from ccta images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373332/
https://www.ncbi.nlm.nih.gov/pubmed/30488262
http://dx.doi.org/10.1007/s11548-018-1891-7
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