Infarct segmentation of the left ventricle using graph-cuts

Rashed Karim; Zhong Chen; Samantha Obom; Ying-Liang Ma; Prince Acheampong; Harminder Gill; Jaspal Gill; C. Aldo Rinaldi; Mark O'Neill; Reza Razavi; Tobias Schaeffter; Kawal S. Rhode

doi:10.1007/978-3-642-36961-2_9

Infarct segmentation of the left ventricle using graph-cuts

Rashed Karim, Zhong Chen, Samantha Obom, Ying-Liang Ma, Prince Acheampong, Harminder Gill, Jaspal Gill, C. Aldo Rinaldi, Mark O'Neill, Reza Razavi, Tobias Schaeffter, Kawal S. Rhode

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for imaging left ventricular (LV) infarct. Existing techniques for LV infarct segmentation are primarily threshold-based making them prone to high user variability. In this work, we propose a segmentation algorithm that can learn from training images and segment based on this training model. This is implemented as a Markov random field (MRF) based energy formulation solved using graph-cuts. A good agreement was found with the Full-Width-at-Half-Maximum (FWHM) technique.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Subtitle of host publication	STACOM 2012
Editors	Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	71–79
Number of pages	9
ISBN (Electronic)	978-3-642-36961-2
ISBN (Print)	978-3-642-36960-5
DOIs	https://doi.org/10.1007/978-3-642-36961-2_9
Publication status	Published - Apr 2013

Publication series

Name	Lecture Notes in Computer Science
Volume	7746

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10.1007/978-3-642-36961-2_9

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Karim, R., Chen, Z., Obom, S., Ma, Y-L., Acheampong, P., Gill, H., Gill, J., Rinaldi, C. A., O'Neill, M., Razavi, R., Schaeffter, T., & Rhode, K. S. (2013). Infarct segmentation of the left ventricle using graph-cuts. In O. Camara, T. Mansi, M. Pop, K. Rhode, M. Sermesant, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2012 (pp. 71–79). (Lecture Notes in Computer Science; Vol. 7746). Springer. https://doi.org/10.1007/978-3-642-36961-2_9

Karim, Rashed ; Chen, Zhong ; Obom, Samantha et al. / Infarct segmentation of the left ventricle using graph-cuts. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2012. editor / Oscar Camara ; Tommaso Mansi ; Mihaela Pop ; Kawal Rhode ; Maxime Sermesant ; Alistair Young. Berlin, Heidelberg : Springer, 2013. pp. 71–79 (Lecture Notes in Computer Science).

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title = "Infarct segmentation of the left ventricle using graph-cuts",

abstract = "Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for imaging left ventricular (LV) infarct. Existing techniques for LV infarct segmentation are primarily threshold-based making them prone to high user variability. In this work, we propose a segmentation algorithm that can learn from training images and segment based on this training model. This is implemented as a Markov random field (MRF) based energy formulation solved using graph-cuts. A good agreement was found with the Full-Width-at-Half-Maximum (FWHM) technique.",

author = "Rashed Karim and Zhong Chen and Samantha Obom and Ying-Liang Ma and Prince Acheampong and Harminder Gill and Jaspal Gill and Rinaldi, {C. Aldo} and Mark O'Neill and Reza Razavi and Tobias Schaeffter and Rhode, {Kawal S.}",

year = "2013",

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doi = "10.1007/978-3-642-36961-2_9",

language = "English",

isbn = "978-3-642-36960-5",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "71–79",

editor = "Oscar Camara and Tommaso Mansi and Mihaela Pop and Kawal Rhode and Maxime Sermesant and Alistair Young",

booktitle = "Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges",

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Karim, R, Chen, Z, Obom, S, Ma, Y-L, Acheampong, P, Gill, H, Gill, J, Rinaldi, CA, O'Neill, M, Razavi, R, Schaeffter, T & Rhode, KS 2013, Infarct segmentation of the left ventricle using graph-cuts. in O Camara, T Mansi, M Pop, K Rhode, M Sermesant & A Young (eds), Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2012. Lecture Notes in Computer Science, vol. 7746, Springer, Berlin, Heidelberg, pp. 71–79. https://doi.org/10.1007/978-3-642-36961-2_9

Infarct segmentation of the left ventricle using graph-cuts. / Karim, Rashed; Chen, Zhong; Obom, Samantha et al.

Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2012. ed. / Oscar Camara; Tommaso Mansi; Mihaela Pop; Kawal Rhode; Maxime Sermesant; Alistair Young. Berlin, Heidelberg : Springer, 2013. p. 71–79 (Lecture Notes in Computer Science; Vol. 7746).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Infarct segmentation of the left ventricle using graph-cuts

AU - Karim, Rashed

AU - Chen, Zhong

AU - Obom, Samantha

AU - Ma, Ying-Liang

AU - Acheampong, Prince

AU - Gill, Harminder

AU - Gill, Jaspal

AU - Rinaldi, C. Aldo

AU - O'Neill, Mark

AU - Razavi, Reza

AU - Schaeffter, Tobias

AU - Rhode, Kawal S.

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N2 - Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for imaging left ventricular (LV) infarct. Existing techniques for LV infarct segmentation are primarily threshold-based making them prone to high user variability. In this work, we propose a segmentation algorithm that can learn from training images and segment based on this training model. This is implemented as a Markov random field (MRF) based energy formulation solved using graph-cuts. A good agreement was found with the Full-Width-at-Half-Maximum (FWHM) technique.

AB - Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for imaging left ventricular (LV) infarct. Existing techniques for LV infarct segmentation are primarily threshold-based making them prone to high user variability. In this work, we propose a segmentation algorithm that can learn from training images and segment based on this training model. This is implemented as a Markov random field (MRF) based energy formulation solved using graph-cuts. A good agreement was found with the Full-Width-at-Half-Maximum (FWHM) technique.

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SN - 978-3-642-36960-5

T3 - Lecture Notes in Computer Science

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EP - 79

BT - Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges

A2 - Camara, Oscar

A2 - Mansi, Tommaso

A2 - Pop, Mihaela

A2 - Rhode, Kawal

A2 - Sermesant, Maxime

A2 - Young, Alistair

PB - Springer

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ER -

Karim R, Chen Z, Obom S, Ma Y-L, Acheampong P, Gill H et al. Infarct segmentation of the left ventricle using graph-cuts. In Camara O, Mansi T, Pop M, Rhode K, Sermesant M, Young A, editors, Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: STACOM 2012. Berlin, Heidelberg: Springer. 2013. p. 71–79. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-36961-2_9

Infarct segmentation of the left ventricle using graph-cuts

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