Renal cortex segmentation using optimal surface search with novel graph construction

Xiuli Li; Xinjian Chen; Jianhua Yao; Xing Zhang; Jie Tian

doi:10.1007/978-3-642-23626-6_48

Renal cortex segmentation using optimal surface search with novel graph construction

Xiuli Li
, Xinjian Chen
, Jianhua Yao
, Xing Zhang
, Jie Tian^*

^*Corresponding author for this work

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

18 Scopus citations

Abstract

In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10% and 0.08%, respectively. The experimental results demonstrate the effectiveness of the proposed method.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Pages	387-394
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-23626-6_48
State	Published - 2011
Externally published	Yes
Event	14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada Duration: 18 Sep 2011 → 22 Sep 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6893 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Country/Territory	Canada
City	Toronto, ON
Period	18/09/11 → 22/09/11

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10.1007/978-3-642-23626-6_48

Cite this

Li, X., Chen, X., Yao, J., Zhang, X., & Tian, J. (2011). Renal cortex segmentation using optimal surface search with novel graph construction. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings (PART 3 ed., pp. 387-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-23626-6_48

Li, Xiuli ; Chen, Xinjian ; Yao, Jianhua et al. / Renal cortex segmentation using optimal surface search with novel graph construction. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3. ed. 2011. pp. 387-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

@inproceedings{e258736ccf464d06a8e4a2e0229ea4e9,

title = "Renal cortex segmentation using optimal surface search with novel graph construction",

abstract = "In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10\% and 0.08\%, respectively. The experimental results demonstrate the effectiveness of the proposed method.",

author = "Xiuli Li and Xinjian Chen and Jianhua Yao and Xing Zhang and Jie Tian",

year = "2011",

doi = "10.1007/978-3-642-23626-6\_48",

language = "英语",

isbn = "9783642236259",

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

number = "PART 3",

pages = "387--394",

booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings",

edition = "PART 3",

note = "14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 ; Conference date: 18-09-2011 Through 22-09-2011",

}

Li, X, Chen, X, Yao, J, Zhang, X & Tian, J 2011, Renal cortex segmentation using optimal surface search with novel graph construction. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6893 LNCS, pp. 387-394, 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011, Toronto, ON, Canada, 18/09/11. https://doi.org/10.1007/978-3-642-23626-6_48

Renal cortex segmentation using optimal surface search with novel graph construction. / Li, Xiuli; Chen, Xinjian; Yao, Jianhua et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3. ed. 2011. p. 387-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3).

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

TY - GEN

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AU - Li, Xiuli

AU - Chen, Xinjian

AU - Yao, Jianhua

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AB - In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10% and 0.08%, respectively. The experimental results demonstrate the effectiveness of the proposed method.

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M3 - 会议稿件

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Li X, Chen X, Yao J, Zhang X, Tian J. Renal cortex segmentation using optimal surface search with novel graph construction. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings. PART 3 ed. 2011. p. 387-394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-23626-6_48

Renal cortex segmentation using optimal surface search with novel graph construction

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