Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation

Chenglong Wang; Masahiro Oda; Yuichiro Hayashi; Yasushi Yoshino; Tokunori Yamamoto; Alejandro F. Frangi; Kensaku Mori

doi:10.1007/978-3-319-46726-9_41

Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation

Chenglong Wang^*, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Alejandro F. Frangi, Kensaku Mori

^*Corresponding author for this work

Nagoya University

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

Abstract

Renal artery segmentation remained a big challenging due to its low contrast. In this paper,we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scalevalued images. Conventional graph-cut methods only use intensity information,which may result in failing in segmentation of small blood vessels. To overcome this drawback,this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about 80% of renal arteries with 1mm precision in diameter.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
Publisher	Springer-Verlag Italia
Pages	353-361
Number of pages	9
ISBN (Print)	9783319467252
DOIs	https://doi.org/10.1007/978-3-319-46726-9_41
Publication status	Published - 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9902 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Blood vessel segmentation
Graph-cut
Hessian matrix
Renal artery
Riemannian manifold
Tensor

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10.1007/978-3-319-46726-9_41

Cite this

Wang, C., Oda, M., Hayashi, Y., Yoshino, Y., Yamamoto, T., Frangi, A. F., & Mori, K. (2016). Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation. In L. Joskowicz, M. R. Sabuncu, W. Wells, G. Unal, & S. Ourselin (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 353-361). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-46726-9_41

Wang, Chenglong ; Oda, Masahiro ; Hayashi, Yuichiro et al. / Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal ; Sebastian Ourselin. Springer-Verlag Italia, 2016. pp. 353-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation",

abstract = "Renal artery segmentation remained a big challenging due to its low contrast. In this paper,we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scalevalued images. Conventional graph-cut methods only use intensity information,which may result in failing in segmentation of small blood vessels. To overcome this drawback,this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about 80% of renal arteries with 1mm precision in diameter.",

keywords = "Blood vessel segmentation, Graph-cut, Hessian matrix, Renal artery, Riemannian manifold, Tensor",

author = "Chenglong Wang and Masahiro Oda and Yuichiro Hayashi and Yasushi Yoshino and Tokunori Yamamoto and Frangi, {Alejandro F.} and Kensaku Mori",

note = "Funding Information: Parts of this research were supported by MEXT and JSPS KAKENHI (26108006, 26560255, 25242047), Kayamori Foundation and the JSPS Bilateral International Collaboration Grants. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",

year = "2016",

doi = "10.1007/978-3-319-46726-9_41",

language = "English",

isbn = "9783319467252",

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

publisher = "Springer-Verlag Italia",

pages = "353--361",

editor = "Leo Joskowicz and Sabuncu, {Mert R.} and William Wells and Gozde Unal and Sebastian Ourselin",

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Wang, C, Oda, M, Hayashi, Y, Yoshino, Y, Yamamoto, T, Frangi, AF & Mori, K 2016, Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation. in L Joskowicz, MR Sabuncu, W Wells, G Unal & S Ourselin (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9902 LNCS, Springer-Verlag Italia, pp. 353-361. https://doi.org/10.1007/978-3-319-46726-9_41

Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation. / Wang, Chenglong; Oda, Masahiro; Hayashi, Yuichiro et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal; Sebastian Ourselin. Springer-Verlag Italia, 2016. p. 353-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS).

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

TY - GEN

T1 - Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation

AU - Wang, Chenglong

AU - Oda, Masahiro

AU - Hayashi, Yuichiro

AU - Yoshino, Yasushi

AU - Yamamoto, Tokunori

AU - Frangi, Alejandro F.

AU - Mori, Kensaku

N1 - Funding Information: Parts of this research were supported by MEXT and JSPS KAKENHI (26108006, 26560255, 25242047), Kayamori Foundation and the JSPS Bilateral International Collaboration Grants. Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Renal artery segmentation remained a big challenging due to its low contrast. In this paper,we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scalevalued images. Conventional graph-cut methods only use intensity information,which may result in failing in segmentation of small blood vessels. To overcome this drawback,this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about 80% of renal arteries with 1mm precision in diameter.

AB - Renal artery segmentation remained a big challenging due to its low contrast. In this paper,we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scalevalued images. Conventional graph-cut methods only use intensity information,which may result in failing in segmentation of small blood vessels. To overcome this drawback,this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about 80% of renal arteries with 1mm precision in diameter.

KW - Blood vessel segmentation

KW - Graph-cut

KW - Hessian matrix

KW - Renal artery

KW - Riemannian manifold

KW - Tensor

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AN - SCOPUS:84996555068

SN - 9783319467252

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

SP - 353

EP - 361

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Wells, William

A2 - Unal, Gozde

A2 - Ourselin, Sebastian

PB - Springer-Verlag Italia

ER -

Wang C, Oda M, Hayashi Y, Yoshino Y, Yamamoto T, Frangi AF et al. Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation. In Joskowicz L, Sabuncu MR, Wells W, Unal G, Ourselin S, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer-Verlag Italia. 2016. p. 353-361. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46726-9_41

Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation

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