Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images u...

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Published inComputational and mathematical methods in medicine Vol. 2016; no. 2016; pp. 1 - 14
Main Authors Zhang, Yanhua, Wu, Shuicai, Zhou, Zhuhuang, Wu, Weiwei
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
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Online AccessGet full text
ISSN1748-670X
1748-6718
1748-6718
DOI10.1155/2016/9093721

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Abstract Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.
AbstractList Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.
Author Zhou, Zhuhuang
Zhang, Yanhua
Wu, Shuicai
Wu, Weiwei
AuthorAffiliation 1 College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
2 College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
AuthorAffiliation_xml – name: 2 College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/27127536$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1109/tip.2015.2481326
10.1007/s11263-006-7934-5
10.1007/s11548-009-0293-2
10.1007/s00330-007-0587-0
10.1016/j.ijleo.2013.10.049
10.1109/tbme.2010.2056369
10.1145/1015706.1015720
10.1016/j.compbiomed.2008.04.006
10.1007/s10462-011-9220-3
10.1118/1.3395579
10.1118/1.3682171
10.1148/radiol.2403050850
10.1109/tpami.2012.120
10.4236/jcc.2014.22001
10.1148/rg.263055186
10.1155/2013/958398
10.1109/tpami.2004.60
10.1155/2014/182909
10.1007/s11548-015-1201-6
10.1109/tsmc.1979.4310076
10.1118/1.3590374
10.1016/j.artmed.2008.07.020
10.1118/1.4866837
10.1016/j.bspc.2012.04.005
10.1002/ijc.29210
10.1155/2014/198015
10.1118/1.4934834
10.1109/79.543975
10.1007/s11760-011-0223-y
10.1145/1015706.1015719
10.1016/j.compmedimag.2015.01.006
10.1118/1.3284530
10.1109/tmi.2009.2013851
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Copyright Copyright © 2016 Weiwei Wu et al.
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References (23) 2012; 7
(20) 2015; 24
(1) 2015; 136
(3) 2006; 240
(8) 2014; 2014
(7) 2013; 7
Zhao Y. Zan Y. Wang X. Li G. Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation Proceedings of the Chinese Control and Decision Conference (CCDC '10) May 2010 Xuzhou, China 3423 3427 10.1109/ccdc.2010.5498558 2-s2.0-77955406531
Li C. Wang X. Eberl S. Fulham M. Yin Y. Feng D. Fully automated liver segmentation for low- and high-contrast ct volumes based on probabilistic atlases Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10) September 2010 Hong Kong 1733 1736 10.1109/icip.2010.5654434 2-s2.0-78651252814
(37) 1996; 13
(29) 2012; 34
(36) 2009; 4
(33) 1979; 9
Priyadarsini S. Selvathi D. Survey on segmentation of liver from CT images Proceedings of the IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT '12) August 2012 Ramanathapuram, India 234 238 10.1109/icaccct.2012.6320777 2-s2.0-84869405542
Massoptier L. Casciaro S. Fully automatic liver segmentation through graph-cut technique Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '07) August 2007 Lyon, France 5243 5246 10.1109/iembs.2007.4353524 2-s2.0-57649149659
(31) 2004; 26
(38) 2004; 23
(42) 2015; 10
(40) 2007; 17
(24) 2015; 42
Lucchi A. Smith K. Achanta R. Lepetit V. Fua P. A fully automated approach to segmentation of irregularly shaped cellular structures in EM images 13 Proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '10) September 2010 Beijing, China 463 471
(4) 2009; 28
(16) 2010; 37
(22) 2013; 2013
(2) 2009; 45
(14) 2010; 57
(10) 2014; 41
(21) 2012; 39
Arthur D. Vassilvitskii S. K-means++: the advantages of careful seeding Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '07) January 2007 New Orleans, La, USA 1027 1035
(25) 2014; 2
(18) 2014; 2014
(27) 2012; 37
(34) 2008; 38
(30) 2004; 23
(41) 2011; 38
(28) 2006; 26
(11) 2010; 37
Zhang H. Yang L. Foran D. J. Nosher J. L. Yim P. J. 3D segmentation of the liver using free-form deformation based on boosting and deformation gradients Proceedings of the 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09) June-July 2009 Boston, Mass, USA 494 497 10.1109/ISBI.2009.5193092
Chen Y. Wang Z. Zhao W. Yang X. Liver segmentation from CT images based on region growing method Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '09) June 2009 Beijing, China 1 4 10.1109/icbbe.2009.5163018 2-s2.0-72749087348
(12) 2015; 43
(6) 2014; 125
(32) 2006; 70
Kainmüller D. Lange T. Lamecker H. Shape constrained automatic segmentation of the liver based on a heuristic intensity model Proceedings of the MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge October 2007 Berlin, Germany 109 116
22
23
24
25
27
28
29
30
31
10
32
11
33
12
35
14
36
37
16
39
18
1
2
3
4
6
7
8
40
41
20
42
21
25728595 - Comput Med Imaging Graph. 2015 Jul;43:1-14
16702462 - Radiographics. 2006 May-Jun;26(3):905-22
26415173 - IEEE Trans Image Process. 2015 Dec;24(12):5315-29
24066017 - Comput Math Methods Med. 2013;2013:958398
24694159 - Med Phys. 2014 Apr;41(4):043502
20527550 - Med Phys. 2010 May;37(5):2159-66
26632041 - Med Phys. 2015 Dec;42(12):6840-52
20033595 - Int J Comput Assist Radiol Surg. 2009 May;4(3):287-97
25220842 - Int J Cancer. 2015 Mar 1;136(5):E359-86
20879348 - Med Image Comput Comput Assist Interv. 2010;13(Pt 2):463-71
19997530 - Proc IEEE Int Symp Biomed Imaging. 2009;5193092:494-497
15742889 - IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1124-37
25105118 - Biomed Res Int. 2014;2014:198015
17429644 - Eur Radiol. 2007 Aug;17(8):2062-70
19059767 - Artif Intell Med. 2009 Feb-Mar;45(2-3):185-96
25903775 - Int J Comput Assist Radiol Surg. 2015 Jun;10(6):879-89
20615804 - IEEE Trans Biomed Eng. 2010 Oct;57(10):2622-6
20229887 - Med Phys. 2010 Feb;37(2):771-83
19211338 - IEEE Trans Med Imaging. 2009 Aug;28(8):1251-65
25276219 - Comput Math Methods Med. 2014;2014:182909
22380370 - Med Phys. 2012 Mar;39(3):1361-73
21815399 - Med Phys. 2011 Jun;38(6):3246-59
16857979 - Radiology. 2006 Sep;240(3):743-8
18550045 - Comput Biol Med. 2008 Jul;38(7):765-84
22641706 - IEEE Trans Pattern Anal Mach Intell. 2012 Nov;34(11):2274-82
18003190 - Conf Proc IEEE Eng Med Biol Soc. 2007;2007:5243-6
References_xml – volume: 26
  start-page: 1124
  issue: 9
  year: 2004
  end-page: 1137
  ident: 31
  article-title: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision
– volume: 43
  start-page: 1
  year: 2015
  end-page: 14
  ident: 12
  article-title: Iterative mesh transformation for 3D segmentation of livers with cancers in CT images
– volume: 13
  start-page: 47
  issue: 6
  year: 1996
  end-page: 60
  ident: 37
  article-title: The expectation-maximization algorithm
– volume: 17
  start-page: 2062
  issue: 8
  year: 2007
  end-page: 2070
  ident: 40
  article-title: Actual role of radiofrequency ablation of liver metastases
– volume: 37
  start-page: 2159
  issue: 5
  year: 2010
  end-page: 2166
  ident: 11
  article-title: Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms
– volume: 240
  start-page: 743
  issue: 3
  year: 2006
  end-page: 748
  ident: 3
  article-title: Automated hepatic volumetry for living related liver transplantation at multisection CT
– volume: 23
  start-page: 303
  issue: 3
  year: 2004
  end-page: 308
  ident: 38
  article-title: Lazy snapping
– volume: 7
  start-page: 163
  issue: 1
  year: 2013
  end-page: 172
  ident: 7
  article-title: Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases
– volume: 37
  start-page: 83
  issue: 2
  year: 2012
  end-page: 95
  ident: 27
  article-title: Survey on liver CT image segmentation methods
– volume: 38
  start-page: 3246
  issue: 6
  year: 2011
  end-page: 3259
  ident: 41
  article-title: Computer-assisted trajectory planning for percutaneous needle insertions
– volume: 2
  start-page: 1
  issue: 2
  year: 2014
  end-page: 7
  ident: 25
  article-title: Review on the methods of automatic liver segmentation from abdominal images
– volume: 136
  start-page: e359
  issue: 5
  year: 2015
  end-page: e386
  ident: 1
  article-title: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012
– volume: 28
  start-page: 1251
  issue: 8
  year: 2009
  end-page: 1265
  ident: 4
  article-title: Comparison and evaluation of methods for liver segmentation from CT datasets
– volume: 38
  start-page: 765
  issue: 7
  year: 2008
  end-page: 784
  ident: 34
  article-title: Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation
– volume: 9
  start-page: 62
  issue: 1
  year: 1979
  end-page: 66
  ident: 33
  article-title: A threshold selection method from gray level histogram
– reference: Zhao Y. Zan Y. Wang X. Li G. Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation Proceedings of the Chinese Control and Decision Conference (CCDC '10) May 2010 Xuzhou, China 3423 3427 10.1109/ccdc.2010.5498558 2-s2.0-77955406531
– reference: Arthur D. Vassilvitskii S. K-means++: the advantages of careful seeding Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '07) January 2007 New Orleans, La, USA 1027 1035
– volume: 37
  start-page: 771
  issue: 2
  year: 2010
  end-page: 783
  ident: 16
  article-title: Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation
– volume: 34
  start-page: 2274
  issue: 11
  year: 2012
  end-page: 2281
  ident: 29
  article-title: SLIC superpixels compared to state-of-the-art superpixel methods
– volume: 41
  issue: 4
  year: 2014
  ident: 10
  article-title: A region-appearance-based adaptive variational model for 3D liver segmentation
– volume: 70
  start-page: 109
  issue: 2
  year: 2006
  end-page: 131
  ident: 32
  article-title: Graph cuts and efficient N-D image segmentation
– volume: 2013
  year: 2013
  end-page: 12
  ident: 22
  article-title: Liver segmentation based on snakes model and improved GrowCut algorithm in abdominal CT image
– volume: 23
  start-page: 309
  issue: 3
  year: 2004
  end-page: 314
  ident: 30
  article-title: ‘GrabCut’—interactive foreground extraction using iterated graph cuts
– reference: Massoptier L. Casciaro S. Fully automatic liver segmentation through graph-cut technique Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '07) August 2007 Lyon, France 5243 5246 10.1109/iembs.2007.4353524 2-s2.0-57649149659
– volume: 45
  start-page: 185
  issue: 2-3
  year: 2009
  end-page: 196
  ident: 2
  article-title: Liver segmentation from computed tomography scans: a survey and a new algorithm
– volume: 125
  start-page: 2142
  issue: 9
  year: 2014
  end-page: 2147
  ident: 6
  article-title: The study and application of the improved region growing algorithm for liver segmentation
– reference: Kainmüller D. Lange T. Lamecker H. Shape constrained automatic segmentation of the liver based on a heuristic intensity model Proceedings of the MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge October 2007 Berlin, Germany 109 116
– reference: Li C. Wang X. Eberl S. Fulham M. Yin Y. Feng D. Fully automated liver segmentation for low- and high-contrast ct volumes based on probabilistic atlases Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10) September 2010 Hong Kong 1733 1736 10.1109/icip.2010.5654434 2-s2.0-78651252814
– reference: Lucchi A. Smith K. Achanta R. Lepetit V. Fua P. A fully automated approach to segmentation of irregularly shaped cellular structures in EM images 13 Proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '10) September 2010 Beijing, China 463 471
– reference: Chen Y. Wang Z. Zhao W. Yang X. Liver segmentation from CT images based on region growing method Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '09) June 2009 Beijing, China 1 4 10.1109/icbbe.2009.5163018 2-s2.0-72749087348
– volume: 57
  start-page: 2622
  issue: 10
  year: 2010
  end-page: 2626
  ident: 14
  article-title: Automatic liver segmentation using a statistical shape model with optimal surface detection
– reference: Zhang H. Yang L. Foran D. J. Nosher J. L. Yim P. J. 3D segmentation of the liver using free-form deformation based on boosting and deformation gradients Proceedings of the 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09) June-July 2009 Boston, Mass, USA 494 497 10.1109/ISBI.2009.5193092
– volume: 39
  start-page: 1361
  issue: 3
  year: 2012
  end-page: 1373
  ident: 21
  article-title: Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods
– volume: 42
  start-page: 6840
  issue: 12
  year: 2015
  end-page: 6852
  ident: 24
  article-title: 3D liver segmentation using multiple region appearances and graph cuts
– volume: 26
  start-page: 905
  issue: 3
  year: 2006
  end-page: 922
  ident: 28
  article-title: Volume rendering versus maximum intensity projection in CT angiography: what works best, when, and why
– volume: 2014
  year: 2014
  end-page: 12
  ident: 8
  article-title: A low-interaction automatic 3D liver segmentation method using computed tomography for selective internal radiation therapy
– volume: 24
  start-page: 5315
  issue: 12
  year: 2015
  end-page: 5329
  ident: 20
  article-title: Automatic liver segmentation based on shape constraints and deformable graph cut in CT images
– volume: 7
  start-page: 591
  issue: 6
  year: 2012
  end-page: 598
  ident: 23
  article-title: The domain knowledge based graph-cut model for liver CT segmentation
– volume: 4
  start-page: 287
  issue: 3
  year: 2009
  end-page: 297
  ident: 36
  article-title: Liver segmentation by intensity analysis and anatomical information in multi-slice CT images
– reference: Priyadarsini S. Selvathi D. Survey on segmentation of liver from CT images Proceedings of the IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT '12) August 2012 Ramanathapuram, India 234 238 10.1109/icaccct.2012.6320777 2-s2.0-84869405542
– volume: 2014
  year: 2014
  end-page: 16
  ident: 18
  article-title: A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans
– volume: 10
  start-page: 879
  issue: 6
  year: 2015
  end-page: 889
  ident: 42
  article-title: Interactive multi-criteria planning for radiofrequency ablation
– ident: 20
  doi: 10.1109/tip.2015.2481326
– ident: 31
  doi: 10.1007/s11263-006-7934-5
– ident: 39
  doi: 10.1007/s11548-009-0293-2
– ident: 40
  doi: 10.1007/s00330-007-0587-0
– ident: 6
  doi: 10.1016/j.ijleo.2013.10.049
– ident: 14
  doi: 10.1109/tbme.2010.2056369
– ident: 35
  doi: 10.1145/1015706.1015720
– ident: 33
  doi: 10.1016/j.compbiomed.2008.04.006
– ident: 27
  doi: 10.1007/s10462-011-9220-3
– ident: 11
  doi: 10.1118/1.3395579
– ident: 21
  doi: 10.1118/1.3682171
– ident: 3
  doi: 10.1148/radiol.2403050850
– ident: 29
  doi: 10.1109/tpami.2012.120
– ident: 25
  doi: 10.4236/jcc.2014.22001
– ident: 28
  doi: 10.1148/rg.263055186
– ident: 22
  doi: 10.1155/2013/958398
– ident: 30
  doi: 10.1109/tpami.2004.60
– ident: 18
  doi: 10.1155/2014/182909
– ident: 42
  doi: 10.1007/s11548-015-1201-6
– ident: 32
  doi: 10.1109/tsmc.1979.4310076
– ident: 41
  doi: 10.1118/1.3590374
– ident: 2
  doi: 10.1016/j.artmed.2008.07.020
– ident: 10
  doi: 10.1118/1.4866837
– ident: 23
  doi: 10.1016/j.bspc.2012.04.005
– ident: 1
  doi: 10.1002/ijc.29210
– ident: 8
  doi: 10.1155/2014/198015
– ident: 24
  doi: 10.1118/1.4934834
– ident: 37
  doi: 10.1109/79.543975
– ident: 7
  doi: 10.1007/s11760-011-0223-y
– ident: 36
  doi: 10.1145/1015706.1015719
– ident: 12
  doi: 10.1016/j.compmedimag.2015.01.006
– ident: 16
  doi: 10.1118/1.3284530
– ident: 4
  doi: 10.1109/tmi.2009.2013851
– reference: 16702462 - Radiographics. 2006 May-Jun;26(3):905-22
– reference: 15742889 - IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1124-37
– reference: 19059767 - Artif Intell Med. 2009 Feb-Mar;45(2-3):185-96
– reference: 17429644 - Eur Radiol. 2007 Aug;17(8):2062-70
– reference: 24694159 - Med Phys. 2014 Apr;41(4):043502
– reference: 25105118 - Biomed Res Int. 2014;2014:198015
– reference: 20229887 - Med Phys. 2010 Feb;37(2):771-83
– reference: 20879348 - Med Image Comput Comput Assist Interv. 2010;13(Pt 2):463-71
– reference: 20527550 - Med Phys. 2010 May;37(5):2159-66
– reference: 25276219 - Comput Math Methods Med. 2014;2014:182909
– reference: 26415173 - IEEE Trans Image Process. 2015 Dec;24(12):5315-29
– reference: 16857979 - Radiology. 2006 Sep;240(3):743-8
– reference: 20615804 - IEEE Trans Biomed Eng. 2010 Oct;57(10):2622-6
– reference: 25728595 - Comput Med Imaging Graph. 2015 Jul;43:1-14
– reference: 24066017 - Comput Math Methods Med. 2013;2013:958398
– reference: 20033595 - Int J Comput Assist Radiol Surg. 2009 May;4(3):287-97
– reference: 19211338 - IEEE Trans Med Imaging. 2009 Aug;28(8):1251-65
– reference: 21815399 - Med Phys. 2011 Jun;38(6):3246-59
– reference: 19997530 - Proc IEEE Int Symp Biomed Imaging. 2009;5193092:494-497
– reference: 25903775 - Int J Comput Assist Radiol Surg. 2015 Jun;10(6):879-89
– reference: 22641706 - IEEE Trans Pattern Anal Mach Intell. 2012 Nov;34(11):2274-82
– reference: 18550045 - Comput Biol Med. 2008 Jul;38(7):765-84
– reference: 22380370 - Med Phys. 2012 Mar;39(3):1361-73
– reference: 18003190 - Conf Proc IEEE Eng Med Biol Soc. 2007;2007:5243-6
– reference: 26632041 - Med Phys. 2015 Dec;42(12):6840-52
– reference: 25220842 - Int J Cancer. 2015 Mar 1;136(5):E359-86
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Snippet Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver...
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SubjectTerms Abdomen - pathology
Algorithms
Anisotropy
Cluster Analysis
Computer Simulation
Cone-Beam Computed Tomography
Contrast Media - chemistry
Databases, Factual
Diagnosis, Computer-Assisted
Humans
Image Processing, Computer-Assisted - methods
Liver - diagnostic imaging
Liver - pathology
Models, Statistical
Normal Distribution
Pattern Recognition, Automated
Probability
Reproducibility of Results
Tomography, X-Ray Computed
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Title Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts
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https://dx.doi.org/10.1155/2016/9093721
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