Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts

Andrés M. Arias Lorza; Diego D. B. Carvalho; Jens Petersen; Anouk C. van Dijk; Aad van der Lugt; Wiro J. Niessen; Stefan Klein; Marleen de Bruijne

doi:10.1007/978-3-642-40763-5_67

Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts

Andrés M. Arias Lorza, Diego D. B. Carvalho, Jens Petersen, Anouk C. van Dijk, Aad van der Lugt, Wiro J. Niessen, Stefan Klein, Marleen de Bruijne

Datalogisk Institut

9 Citationer (Scopus)

1100 Downloads (Pure)

Abstract

We present a new approach for automated segmentation of the carotid lumen bifurcation from 3D free-hand ultrasound using a 3D surface graph cut method. The method requires only the manual selection of single seed points in the internal, external, and common carotid arteries. Subsequently, the centerline between these points is automatically traced, and the optimal lumen surface is found around the centerline using graph cuts. To refine the result, the latter process was iterated. The method was tested on twelve carotid arteries from six subjects including three patients with a moderate carotid artery stenosis. Our method successfully segmented the lumen in all cases. We obtained an average dice overlap with respect to a manual segmentation of 84% for healthy volunteers. For the patient data, we obtained a dice overlap of 66.7%.

Originalsprog	Dansk
Titel	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013 : 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II
Redaktører	Kensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, Nassir Navab
Antal sider	8
Forlag	Springer
Publikationsdato	2013
Sider	542-549
ISBN (Trykt)	978-3-642-40762-8
ISBN (Elektronisk)	978-3-642-40763-5
DOI	https://doi.org/10.1007/978-3-642-40763-5_67
Status	Udgivet - 2013
Begivenhed	16th International Conference on Medical Image Computing and Computer-Assisted Intervention - Nagoya, Japan Varighed: 22 sep. 2013 → 26 sep. 2013 Konferencens nummer: 16

Konference

Konference	16th International Conference on Medical Image Computing and Computer-Assisted Intervention
Nummer	16
Land/Område	Japan
By	Nagoya
Periode	22/09/2013 → 26/09/2013

Navn	Lecture notes in computer science
Vol/bind	8150
ISSN	0302-9743

Adgang til dokumentet

10.1007/978-3-642-40763-5_67

arias_miccai_2013Accepteret manuskript, 2,39 MB
Carotid Artery Lumen Segmentation in 3D Free-Hand Ultrasound Images Using Surface Graph CutsForlagets udgivne version, 1,96 MB

https://link.springer.com/content/pdf/10.1007%2F978-3-642-40763-5_67.pdf

Citationsformater

Lorza, A. M. A., Carvalho, D. D. B., Petersen, J., Dijk, A. C. V., Lugt, A. V. D., Niessen, W. J., Klein, S., & de Bruijne, M. (2013). Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts. I K. Mori, I. Sakuma, Y. Sato, C. Barillot, & N. Navab (red.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II (s. 542-549). Springer. https://doi.org/10.1007/978-3-642-40763-5_67

Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts. / Lorza, Andrés M. Arias; Carvalho, Diego D. B.; Petersen, Jens et al.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II. red. / Kensaku Mori; Ichiro Sakuma; Yoshinobu Sato; Christian Barillot; Nassir Navab. Springer, 2013. s. 542-549 (Lecture notes in computer science, Bind 8150).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Lorza, AMA, Carvalho, DDB, Petersen, J, Dijk, ACV, Lugt, AVD, Niessen, WJ, Klein, S & de Bruijne, M 2013, Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts. i K Mori, I Sakuma, Y Sato, C Barillot & N Navab (red), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II. Springer, Lecture notes in computer science, bind 8150, s. 542-549, 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, Nagoya, Japan, 22/09/2013. https://doi.org/10.1007/978-3-642-40763-5_67

Lorza AMA, Carvalho DDB, Petersen J, Dijk ACV, Lugt AVD, Niessen WJ et al. Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts. I Mori K, Sakuma I, Sato Y, Barillot C, Navab N, red., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II. Springer. 2013. s. 542-549. (Lecture notes in computer science, Bind 8150). doi: 10.1007/978-3-642-40763-5_67

Lorza, Andrés M. Arias ; Carvalho, Diego D. B. ; Petersen, Jens et al. / Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II. red. / Kensaku Mori ; Ichiro Sakuma ; Yoshinobu Sato ; Christian Barillot ; Nassir Navab. Springer, 2013. s. 542-549 (Lecture notes in computer science, Bind 8150).

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abstract = "We present a new approach for automated segmentation of the carotid lumen bifurcation from 3D free-hand ultrasound using a 3D surface graph cut method. The method requires only the manual selection of single seed points in the internal, external, and common carotid arteries. Subsequently, the centerline between these points is automatically traced, and the optimal lumen surface is found around the centerline using graph cuts. To refine the result, the latter process was iterated. The method was tested on twelve carotid arteries from six subjects including three patients with a moderate carotid artery stenosis. Our method successfully segmented the lumen in all cases. We obtained an average dice overlap with respect to a manual segmentation of 84% for healthy volunteers. For the patient data, we obtained a dice overlap of 66.7%.",

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