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Published in: International Journal of Computer Assisted Radiology and Surgery 2/2019

Open Access 01-02-2019 | Original Article

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

Authors: Qing Cao, Alexander Broersen, Pieter H. Kitslaar, Boudewijn P. F. Lelieveldt, Jouke Dijkstra

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2019

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Abstract

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.
Appendix
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Literature
4.
go back to reference Zheng Y, Loziczonek M, Georgescu B, Zhou SK, Higuera FV, Comaniciu D (2011) Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes. In: Proceedings of the SPIE 7962, medical imaging 2011: image processing, Lake Buena Vista (Orlando), FL, US, 11 March 2011, p 79621 K. https://doi.org/10.1117/12.877233 Zheng Y, Loziczonek M, Georgescu B, Zhou SK, Higuera FV, Comaniciu D (2011) Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes. In: Proceedings of the SPIE 7962, medical imaging 2011: image processing, Lake Buena Vista (Orlando), FL, US, 11 March 2011, p 79621 K. https://​doi.​org/​10.​1117/​12.​877233
8.
9.
go back to reference Cao Q, Broersen A, Kitslaar PH, Lelieveldt BPF, Dijkstra J (2018) A quality score for coronary artery tree extraction results. In: Proceedings of the SPIE 10575, medical imaging 2018: computer-aided diagnosis, Houston, TX, US, 27 Feb 2018, p 105750 V. https://doi.org/10.1117/12.2292430 Cao Q, Broersen A, Kitslaar PH, Lelieveldt BPF, Dijkstra J (2018) A quality score for coronary artery tree extraction results. In: Proceedings of the SPIE 10575, medical imaging 2018: computer-aided diagnosis, Houston, TX, US, 27 Feb 2018, p 105750 V. https://​doi.​org/​10.​1117/​12.​2292430
11.
12.
go back to reference Austen WG, Edwards JE, Frye RL, Gensini GG, Gott VL, Griffith LS, McGoon DC, Murphy ML, Roe BB (1975) A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation 51(4):5–40. https://doi.org/10.1161/01.cir.51.4.5 CrossRefPubMed Austen WG, Edwards JE, Frye RL, Gensini GG, Gott VL, Griffith LS, McGoon DC, Murphy ML, Roe BB (1975) A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation 51(4):5–40. https://​doi.​org/​10.​1161/​01.​cir.​51.​4.​5 CrossRefPubMed
14.
go back to reference Kirisli HA, Schaap M, Metz CT, Dharampal AS, Meijboom WB, Papadopoulou SL, Dedic A, Nieman K, de Graaf MA, Meijs MF, Cramer MJ, Broersen A, Cetin S, Eslami A, Florez-Valencia L, Lor KL, Matuszewski B, Melki I, Mohr B, Oksuz I, Shahzad R, Wang C, Kitslaar PH, Unal G, Katouzian A, Orkisz M, Chen CM, Precioso F, Najman L, Masood S, Unay D, van Vliet L, Moreno R, Goldenberg R, Vucini E, Krestin GP, Niessen WJ, van Walsum T (2013) Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med Image Anal 17(8):859–876. https://doi.org/10.1016/j.media.2013.05.007 CrossRefPubMed Kirisli HA, Schaap M, Metz CT, Dharampal AS, Meijboom WB, Papadopoulou SL, Dedic A, Nieman K, de Graaf MA, Meijs MF, Cramer MJ, Broersen A, Cetin S, Eslami A, Florez-Valencia L, Lor KL, Matuszewski B, Melki I, Mohr B, Oksuz I, Shahzad R, Wang C, Kitslaar PH, Unal G, Katouzian A, Orkisz M, Chen CM, Precioso F, Najman L, Masood S, Unay D, van Vliet L, Moreno R, Goldenberg R, Vucini E, Krestin GP, Niessen WJ, van Walsum T (2013) Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med Image Anal 17(8):859–876. https://​doi.​org/​10.​1016/​j.​media.​2013.​05.​007 CrossRefPubMed
Metadata
Title
A model-guided method for improving coronary artery tree extractions from CCTA images
Authors
Qing Cao
Alexander Broersen
Pieter H. Kitslaar
Boudewijn P. F. Lelieveldt
Jouke Dijkstra
Publication date
01-02-2019
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 2/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1891-7

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