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Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography

Research output: Chapter in Book/Report/Conference proceedingChapter

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Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography. / Karim, Rashed; Chubb, Henry; Staab, Wieland; Aziz, Shadman; Housden, R. James; O’Neill, Mark; Razavi, Reza; Rhode, Kawal.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9126 Springer-Verlag Berlin Heidelberg, 2015. p. 155-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9126).

Research output: Chapter in Book/Report/Conference proceedingChapter

Harvard

Karim, R, Chubb, H, Staab, W, Aziz, S, Housden, RJ, O’Neill, M, Razavi, R & Rhode, K 2015, Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9126, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9126, Springer-Verlag Berlin Heidelberg, pp. 155-163, 8th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2015, Maastricht, Netherlands, 25/06/2015. https://doi.org/10.1007/978-3-319-20309-6_18

APA

Karim, R., Chubb, H., Staab, W., Aziz, S., Housden, R. J., O’Neill, M., ... Rhode, K. (2015). Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9126, pp. 155-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9126). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-20309-6_18

Vancouver

Karim R, Chubb H, Staab W, Aziz S, Housden RJ, O’Neill M et al. Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9126. Springer-Verlag Berlin Heidelberg. 2015. p. 155-163. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-20309-6_18

Author

Karim, Rashed ; Chubb, Henry ; Staab, Wieland ; Aziz, Shadman ; Housden, R. James ; O’Neill, Mark ; Razavi, Reza ; Rhode, Kawal. / Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9126 Springer-Verlag Berlin Heidelberg, 2015. pp. 155-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex Download

@inbook{690b9dafb8b94f89b77d932d9f22ffee,
title = "Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography",
abstract = "Magnetic resonance angiography (MRA) scans provide excellent chamber and venous anatomy. However, they have traditionally been acquired in breath-hold and are not cardiac-gated. This has made it difficult to use them in conjunction with late gadolinium enhancement (LGE) scans for reconstructing fibrosis/scar on 3D left atrium (LA) anatomy. This work proposes an image processing algorithm for segmenting the LA from a novel MRA sequence which is both ECG-gated and respiratorygated allowing reliable 3D reconstructions with LGE. The algorithm implements image partitioning using discrete Morse theory on digital images. It is evaluated in the context of creating 3D reconstructions of scar/fibrosis with LGE.",
keywords = "Delayed-enhancement MRI, Image segmentation, Left atrium, Magnetic resonance angiography",
author = "Rashed Karim and Henry Chubb and Wieland Staab and Shadman Aziz and Housden, {R. James} and Mark O’Neill and Reza Razavi and Kawal Rhode",
year = "2015",
doi = "10.1007/978-3-319-20309-6_18",
language = "English",
isbn = "9783319203089",
volume = "9126",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag Berlin Heidelberg",
pages = "155--163",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Left atrial segmentation from 3D respiratoryand ecg-gated magnetic resonance angiography

AU - Karim, Rashed

AU - Chubb, Henry

AU - Staab, Wieland

AU - Aziz, Shadman

AU - Housden, R. James

AU - O’Neill, Mark

AU - Razavi, Reza

AU - Rhode, Kawal

PY - 2015

Y1 - 2015

N2 - Magnetic resonance angiography (MRA) scans provide excellent chamber and venous anatomy. However, they have traditionally been acquired in breath-hold and are not cardiac-gated. This has made it difficult to use them in conjunction with late gadolinium enhancement (LGE) scans for reconstructing fibrosis/scar on 3D left atrium (LA) anatomy. This work proposes an image processing algorithm for segmenting the LA from a novel MRA sequence which is both ECG-gated and respiratorygated allowing reliable 3D reconstructions with LGE. The algorithm implements image partitioning using discrete Morse theory on digital images. It is evaluated in the context of creating 3D reconstructions of scar/fibrosis with LGE.

AB - Magnetic resonance angiography (MRA) scans provide excellent chamber and venous anatomy. However, they have traditionally been acquired in breath-hold and are not cardiac-gated. This has made it difficult to use them in conjunction with late gadolinium enhancement (LGE) scans for reconstructing fibrosis/scar on 3D left atrium (LA) anatomy. This work proposes an image processing algorithm for segmenting the LA from a novel MRA sequence which is both ECG-gated and respiratorygated allowing reliable 3D reconstructions with LGE. The algorithm implements image partitioning using discrete Morse theory on digital images. It is evaluated in the context of creating 3D reconstructions of scar/fibrosis with LGE.

KW - Delayed-enhancement MRI

KW - Image segmentation

KW - Left atrium

KW - Magnetic resonance angiography

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U2 - 10.1007/978-3-319-20309-6_18

DO - 10.1007/978-3-319-20309-6_18

M3 - Chapter

AN - SCOPUS:84949954306

SN - 9783319203089

SN - 9783319203089

VL - 9126

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 155

EP - 163

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer-Verlag Berlin Heidelberg

ER -

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