Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas

R. Karim; C. Juli; L. Malcolme-Lawes; D. Wyn-Davies; P. Kanagaratnam; N. Peters; D. Rueckert

doi:10.1007/978-3-642-15835-3_14

Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas

R. Karim, C. Juli, L. Malcolme-Lawes, D. Wyn-Davies, P. Kanagaratnam, N. Peters, D. Rueckert

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

13 Scopus citations

Abstract

Left atrium segmentation and the extraction of its geometry remains a challenging problem despite of existing approaches. It is a clinically-relevant important problem with an increasing interest as more research into the mechanism of atrial fibrillation and its recurrence process is undertaken. Contrast-Enhanced (CE) Magnetic Resonance Angiography (MRA) produces excellent images for extracting the atrial geometry. Nevertheless, the variable anatomy of the atrium poses significant challenge for segmentation. To overcome the inherent difficulties with this segmentation, we propose a technique that utilizes the Voronoi subdivision framework for the segmentation. In addition, the segmentation is based on the minimization of a Markov Random Field based energy functional defined within the Voronoi framework. The method also incorporates anatomical priors in the form of a probabilistic atlas. We show how the model is efficient in segmenting atrium images by comparing results from manual segmentations.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart - First Int. Workshop, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conj. with MICCAI 2010, Proc.
Pages	134-143
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-15835-3_14
State	Published - 2010
Externally published	Yes
Event	1st International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conjunction with MICCAI 2010 - Beijing, China Duration: 20 Sep 2010 → 20 Sep 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6364 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conjunction with MICCAI 2010
Country/Territory	China
City	Beijing
Period	20/09/10 → 20/09/10

Keywords

Graph Cuts
Left atrium
Magnetic Resonance Angiography
Segmentation

Access to Document

10.1007/978-3-642-15835-3_14

Cite this

Karim, R., Juli, C., Malcolme-Lawes, L., Wyn-Davies, D., Kanagaratnam, P., Peters, N., & Rueckert, D. (2010). Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas. In Statistical Atlases and Computational Models of the Heart - First Int. Workshop, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conj. with MICCAI 2010, Proc. (pp. 134-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6364 LNCS). https://doi.org/10.1007/978-3-642-15835-3_14

Karim, R. ; Juli, C. ; Malcolme-Lawes, L. et al. / Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas. Statistical Atlases and Computational Models of the Heart - First Int. Workshop, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conj. with MICCAI 2010, Proc.. 2010. pp. 134-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas",

abstract = "Left atrium segmentation and the extraction of its geometry remains a challenging problem despite of existing approaches. It is a clinically-relevant important problem with an increasing interest as more research into the mechanism of atrial fibrillation and its recurrence process is undertaken. Contrast-Enhanced (CE) Magnetic Resonance Angiography (MRA) produces excellent images for extracting the atrial geometry. Nevertheless, the variable anatomy of the atrium poses significant challenge for segmentation. To overcome the inherent difficulties with this segmentation, we propose a technique that utilizes the Voronoi subdivision framework for the segmentation. In addition, the segmentation is based on the minimization of a Markov Random Field based energy functional defined within the Voronoi framework. The method also incorporates anatomical priors in the form of a probabilistic atlas. We show how the model is efficient in segmenting atrium images by comparing results from manual segmentations.",

keywords = "Graph Cuts, Left atrium, Magnetic Resonance Angiography, Segmentation",

author = "R. Karim and C. Juli and L. Malcolme-Lawes and D. Wyn-Davies and P. Kanagaratnam and N. Peters and D. Rueckert",

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doi = "10.1007/978-3-642-15835-3_14",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "1st International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conjunction with MICCAI 2010 ; Conference date: 20-09-2010 Through 20-09-2010",

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Karim, R, Juli, C, Malcolme-Lawes, L, Wyn-Davies, D, Kanagaratnam, P, Peters, N & Rueckert, D 2010, Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas. in Statistical Atlases and Computational Models of the Heart - First Int. Workshop, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conj. with MICCAI 2010, Proc.. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6364 LNCS, pp. 134-143, 1st International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conjunction with MICCAI 2010, Beijing, China, 20/09/10. https://doi.org/10.1007/978-3-642-15835-3_14

Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas. / Karim, R.; Juli, C.; Malcolme-Lawes, L. et al.
Statistical Atlases and Computational Models of the Heart - First Int. Workshop, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conj. with MICCAI 2010, Proc.. 2010. p. 134-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6364 LNCS).

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

TY - GEN

T1 - Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas

AU - Karim, R.

AU - Juli, C.

AU - Malcolme-Lawes, L.

AU - Wyn-Davies, D.

AU - Kanagaratnam, P.

AU - Peters, N.

AU - Rueckert, D.

PY - 2010

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N2 - Left atrium segmentation and the extraction of its geometry remains a challenging problem despite of existing approaches. It is a clinically-relevant important problem with an increasing interest as more research into the mechanism of atrial fibrillation and its recurrence process is undertaken. Contrast-Enhanced (CE) Magnetic Resonance Angiography (MRA) produces excellent images for extracting the atrial geometry. Nevertheless, the variable anatomy of the atrium poses significant challenge for segmentation. To overcome the inherent difficulties with this segmentation, we propose a technique that utilizes the Voronoi subdivision framework for the segmentation. In addition, the segmentation is based on the minimization of a Markov Random Field based energy functional defined within the Voronoi framework. The method also incorporates anatomical priors in the form of a probabilistic atlas. We show how the model is efficient in segmenting atrium images by comparing results from manual segmentations.

AB - Left atrium segmentation and the extraction of its geometry remains a challenging problem despite of existing approaches. It is a clinically-relevant important problem with an increasing interest as more research into the mechanism of atrial fibrillation and its recurrence process is undertaken. Contrast-Enhanced (CE) Magnetic Resonance Angiography (MRA) produces excellent images for extracting the atrial geometry. Nevertheless, the variable anatomy of the atrium poses significant challenge for segmentation. To overcome the inherent difficulties with this segmentation, we propose a technique that utilizes the Voronoi subdivision framework for the segmentation. In addition, the segmentation is based on the minimization of a Markov Random Field based energy functional defined within the Voronoi framework. The method also incorporates anatomical priors in the form of a probabilistic atlas. We show how the model is efficient in segmenting atrium images by comparing results from manual segmentations.

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Karim R, Juli C, Malcolme-Lawes L, Wyn-Davies D, Kanagaratnam P, Peters N et al. Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas. In Statistical Atlases and Computational Models of the Heart - First Int. Workshop, STACOM 2010 and Cardiac Electrophysiological Simulation Challenge, CESC 2010 - Held in Conj. with MICCAI 2010, Proc.. 2010. p. 134-143. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15835-3_14

Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas

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