Blood vessel-based liver segmentation using the portal phase of an abdominal CT dataset

Medical Physics

Volumn 40, Issue 11, 2013, Pages

  Maklad, Ahmed S ^a   Matsuhiro, Mikio ^a   Suzuki, Hidenobu ^a   Kawata, Yoshiki ^a   Niki, Noboru ^a   Satake, Mitsuo ^b   Moriyama, Noriyuki ^c   Utsunomiya, Toru ^a   Shimada, Mitsuo ^a  

^a UNIVERSITY OF TOKUSHIMA   (Japan)

^b NATIONAL CANCER CENTER HOSPITAL EAST   (Japan)

^c Tokyo Midtown Medicine   (Japan)

Author keywords

blood vessel (BV); boundary surface; computed tomography (CT) dataset; liver segmentation; portal phase

Indexed keywords

BLOOD; BLOOD VESSELS; GAUSSIAN DISTRIBUTION; GRAPHIC METHODS; IMAGE SEGMENTATION; LARGE DATASET; LIVER; MECHANICAL VARIABLES MEASUREMENT; MEDICAL IMAGING; TUMORS;

BOUNDARY SURFACES; CLINICAL APPLICATION; GAUSSIAN MIXTURE MODEL; INFERIOR VENA CAVA; LIVER SEGMENTATION; MEDICAL IMAGE COMPUTING; THREEDIMENSIONAL (3-D); VARIATIONAL BAYESIAN;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; AUTOMATION; BAYES THEOREM; BLOOD VESSEL; COMPUTER ASSISTED TOMOGRAPHY; DATA ANALYSIS; HISTOGRAM; IMAGE RECONSTRUCTION; INFERIOR CAVA VEIN; LIVER PARENCHYMA; LIVER SIZE; LIVER TUMOR; MEASUREMENT ERROR; PORTAL VEIN; THREE DIMENSIONAL IMAGING; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; FACTUAL DATABASE; HUMAN; LIVER; NORMAL DISTRIBUTION; PATHOLOGY; RADIOGRAPHY; REPRODUCIBILITY; VASCULARIZATION;

ALGORITHMS; BAYES THEOREM; BLOOD VESSELS; DATABASES, FACTUAL; HUMANS; IMAGING, THREE-DIMENSIONAL; LIVER; LIVER NEOPLASMS; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84889686634     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4823765     Document Type: Article

References (58)

1. F. Wang, K. T. Pan, S. Y. Chu, K. M. Chan, H. S. Chou, T. J. Wu, and W. C. Lee, " Preoperative estimation of the liver graft weight in adult right lobe living donor liver transplantation using maximal portal vein diameters.," Liver Transplant. 17, 373-380 (2011). 10.1002/lt.22274
2. S. Saito, J. Yamanaka, K. Miura, N. Nakao, T. Nagao, T. Sugimoto, T. Hirano, N. Kuroda, Y. Iimuro, and J. Fujimoto, " A novel 3D hepatectomy simulation based on liver circulation: Application to liver resection and transplantation.," Hepatology 41, 1297-1304 (2005). 10.1002/hep.20684
3. T. Heimann, B. v. Ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, A. Beck, C. Becker, R. Beichel, G. Bekes, F. Bello, G. Binnig, H. Bischof, A. Bornik, P. M. Cashman, Y. Chi, A. Cordova, B. M. Dawant, M. Fidrich, J. D. Furst, D. Furukawa, L. Grenacher, J. Hornegger, D. Kainmüller, R. I. Kitney, H. Kobatake, H. Lamecker, T. Lange, J. Lee, B. Lennon, R. Li, S. Li, H. P. Meinzer, G. Nemeth, D. S. Raicu, A. M. Rau, E. M. V. Rikxoort, M. Rousson, L. Rusko, K. A. Saddi, G. Schmidt, D. Seghers, A. Shimizu, P. Slagmolen, E. Sorantin, G. Soza, R. Susomboon, J. M. Waite, A. Wimmer, and I. Wolf, " Comparison and evaluation of methods for liver segmentation from CT datasets.," IEEE Trans. Med. Imaging 28, 1251-1265 (2009). 10.1109/TMI.2009.2013851
4. T. Heimann and H.-P. Meinzer, " Statistical shape models for 3D medical image segmentation: A review.," Med. Image Anal. 13 (4), 543-563 (2009). 10.1016/j.media.2009.05.004
5. P. Campadelli, E. Casiraghi, and A. Esposito, " Liver segmentation from computed tomography scans: A survey and a new algorithm.," Artif. Intell. Med. 45 (2-3), 185-196 (2009). 10.1016/j.artmed.2008.07.020
6. A. Schenk, G. Prause, and H. O. Peitgen, " Efficient semiautomatic segmentation of 3D objects in medical images.," in Proceedings of Medical Image Computing And Computer-Assisted Intervention (MICCAI) 2000, Lecture Notes in Computer Science Vol. 1935 (Springer-Verlag, Berlin Heidelberg, 2000), pp. 71-131.
7. A. Wimmar, G. Soza, and J. Hornegger, " Two-stage Semi-automatic Organ Segmentation Framework using Radial Bias Functions and Level sets. " (available URL: http://sliver07.org/data/2010-01-14-1941.pdf), accessed 30 January 2013 or (available URL: http://www5.informatik.uni-erlangen.de/ Forschung/Publikationen/2007/Wimmer07-TSO.pdf), accessed 30 January 2013.
8. M. Kobashi and L. G. Shapiro, " Knowledge-based organ identification from CT images.," Pattern Recognit. 28 (4), 475-491 (1995). 10.1016/0031-3203(94)00124-5
9. D.-Y. Tsai and N. Tanahashi, " Neural-network-based boundary detection of liver structure in CT images for 3-D visualization.," Proceedings of IEEE International Conference on Neural Networks, IEEE Catalog No. 04CH37541 (Neural Networks, Orlando, FL, 1994), Vol. 6, pp. 3484-3489.
10. T. Heimann, I. Wolf, and H. P. Meinzer, " Active shape models for a fully automated 3D segmentation of the liver-An evaluation on clinical data.," Medical Image Computing and Computer Assisted Intervention, Lecture Notes in Computer Science Vol. 4191 (Springer, Heidelberg, 2006), pp. 41-48.
11. D. Kainmuller, T. Lange, and H. Lamecker, " Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model. " (available URL: http://www.sliver07.org/data/2007-10-22-1942.pdf), accessed 6 November 2012.
12. A. Wimmer, G. Soza, and J. Hornegger, " A Generic Probabilistic Active Shape Model for Organ Segmentation. " (available URL: http://www.sliver07.org/data/2009-03-11-0340.pdf), accessed 6 November 2012.
13. L. Gao, D. G. Heath, and E. K. Fishman, " Abdominal image segmentation using three-dimensional deformable models.," Invest. Radiol. 33 (6), 348-355 (1998). 10.1097/00004424-199806000-00006
14. T. Okada, R. Shimada, Y. Sato, M. Hori, K. Yokota, M. Nakamoto, Y.-W. Chen, H. Nakamura, and S. Tamura, " Automated segmentation of the liver from 3D CT images using probabilistic Atlas and multi-level statistical shape model.," MICCAI 2007, Lecture Notes in Computer Science Vol. 4791 (Springer-Verlag, Berlin Heidelberg, 2007), Part I, pp. 86-93.
15. A. Wimmer, G. Soza, and J. Hornegger, " A generic probabilistic active shape model for organ segmentation.," MICCAI 2009, Lecture Notes in Computer Science Vol. 5762 (Springer-Verlag Berlin, Heidelberg, 2009), Part II, pp. 26-33.
16. J. Peng, " A new convex variational model for liver segmentation.," 21st International Conference on Pattern Recognition (ICPR), (Pattern Recognition (ICPR), 2012 21st International Conference on, Tsukuba, 2012), pp. 3754-3757.
17. M. Erdt, M. Kirschner, S. Steger, and S. Wesarg, " Fast automatic liver segmentation combining learned shape priors with observed shape Deviation.," in Proceedings of the IEEE International Symposium on Computer-Based Medical Systems (CBMS) (Computer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on, Perth, WA, 2010), pp. 249-254.
18. K. Drechsler and C. O. Laura, " Simulation of portal vein clamping and the impact of safety margins for liver resection planning.," Abdominal Imaging. Computational and Clinical Applications Workshop Held on Conjunction with MICCAI 2011, Lecture Notes in Computer Science Vol. 7029 (Springer-Verlag, Berlin Heidelberg, 2012), pp. 149-156.
19. X. Zhou, T. Kitagawa, T. Hara, H. Fujita, X. Zhang, R. Yokoyama, H. Kondo, M. Kanematsu, and H. Hoshi, " Constructing a probabilistic model for automated liver region segmentation using non-contrast X-Ray TorsoCT images.," MICCAI 2006, Lecture Notes in Computer Science Vol. 4191 (Springer-Verlag, Berlin Heidelberg, 2006), pp. 856-863.
20. H. Park, P. H. Bland, and C. R. Meyer, " Construction of an abdominal probabilistic atlas and its application in segmentation.," IEEE Trans. Med. Imaging 22 (4), 483-492 (2003). 10.1109/TMI.2003.809139
21. D. Furukawa, A. Shimizu, and H. Kobatake, " Automatic liver segmentation method based on maximum a posterior probability estimation and level set method.," in MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: A Grand Challenge (2007), pp. 117-124.
22. M. G. Linguraru, J. K. Sandberg, Z. Li, F. Shah, and R. M. Summers, " Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation.," Med. Phys. 37 (2), 771-783 (2010). 10.1118/1.3284530
23. L. Fernandez-de-Manuel, J. L. Rubio, M. J. Ledesma-Carbayo, J. Pascau, J. M. Tellado, E. Ramon, M. Desco, and A. Santos, " 3D liver segmentation in preoperative CT images using a level-sets active surface method.," Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 3625-3628 (2009).
24. J. Lee, N. Kim, H. Lee, J. B. Seo, H. J. Won, Y. M. Shin, Y. G. Shin, and S. H. Kim, " Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images.," Comput. Methods Programs Biomed. 88 (1), 26-38 (2007). 10.1016/j.cmpb.2007.07.005
25. S. Pan and B. M. Dawant, " Automatic 3D segmentation of the liver from abdominal CT images: A level-set approach.," Proc. SPIE 4322, 128-138 (2001). 10.1117/12.431019
26. R. Susomboon, D. S. Raicu, and J. Furst, " A hybrid approach for liver segmentation.," in Proceedings of MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge (2007), pp. 151-160.
27. L. Ruskó, G. Bekes, and M. Fidrich, " Automatic segmentation of the liver from multi- and single-phase.," Med. Image Anal. 13 (6), 871-882 (2009). 10.1016/j.media.2009.07.009
28. Y. Y. Boykov and M.-P. Jolly, " Interactive graph cuts for optimal boundary region segmentation of objects in N-D images.," Proc. Int. Conf. Comput. Vis. I, 105-112 (2001).
29. Y. Boykov and G. Funka-Lea, " Graph cuts and efficient N-D image segmentation.," Int. J. Comput. Vis. 70 (2), 109-131 (2006). 10.1007/s11263-006-7934-5
30. R. Beichel, C. Bauer, A. Bornik, E. Sorantin, and H. Bischof, " Liver segmentation in CT data: A segmentation refinement approach.," 3D Segmentation in the Clinic: A Grand Challenge (2007), pp. 235-245.
31. A. Afifi and T. Nakaguchi, " Liver segmentation approach using graph cuts and iteratively estimated shape and intensity constrains.," Proceedings of the 15th Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (Springer-Verlag, Berlin Heidelberg, 2012), Pt. 2, pp. 395-403.
32. L. Massoptier and S. Casciaro, " Fully automatic liver segmentation through graph-cut technique.," Proceedings of the 29th Annual International Conference of the IEEE -EMBS (Engineering in Medicine and Biology Society, 2007, EMBS 2007, 29th Annual International Conference of the IEEE, Lyon, 2007), pp. 5243-5246.
33. S. Esneault, N. Hraiech, E. Delabrousse, and J.-L. Dillenseger, " Graph cut liver segmentation for interstitial ultrasound therapy.," Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society (Engineering in Medicine and Biology Society, 2007, EMBS 2007, 29th Annual International Conference of the IEEE, Lyon, 2007), pp 5247-5250.
34. Y. Chen, W. Zhao, Q. Wu, Z. Wang, and J. Hu, " Liver segmentation in CT images for intervention using a graph-cut based model.," Abdominal Imaging. Computational and Clinical Applications Workshop Held on Conjunction with MICCAI 2011, Lecture Notes in Computer Science Vol. 7029 (Springer-Verlag, Berlin Heidelberg, 2012), pp. 157-164.
35. M. G. Linguraru, W. J. Richbourg, J. M. Watt, V. Pamulapati, and R. M. Summers, " Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts.," Abdominal Imaging 2011, Lecture Notes in Computer Science Vol. 7029 (Springer-Verlag, Berlin Heidelberg, 2012), pp. 198-206.
36. V. Pamulapati, A. Venkatesan, B. J. Wood, and M. G. Linguraru, " Liver segmental anatomy and analysis from vessel and tumor segmentation via optimized graph cuts.," Abdominal Imaging 2011, Lecture Notes in Computer Science Vol. 7029 (Springer-Verlag, Berlin Heidelberg, 2012), pp 198-206.
37. M. G. Linguraru, W. J. Richbourg, J. Liu, J. M. Watt, V. Pamulapati, S. Wang, and R. M. Summers, " Tumor burden analysis on computed tomography by automated liver and tumor segmentation.," IEEE Trans. Med. Imaging 31 (10), 1965-1976 (2012). 10.1109/TMI.2012.2211887
38. D. A. Oliveira, R. Q. Feitosa, and M. M. Correia, " Segmentation of liver, its vessels and lesions from CT images for surgical planning.," Biomed. Eng. Online (available URL: http://www.biomedical-engineering-online. com/content/10/1/30), accessed 30 September 2013.
39. S.-J. Lim, Y.-Y. Jeong, and Y.-S. Ho, " Automatic liver segmentation for volume measurement in CT Images.," Vis. Commun. Image Represent. 17 (4), 860-875 (2006). 10.1016/j.jvcir.2005.07.001
40. G. Schmidt, M. Athelogou, R. Schönmeyer, R. Korn, and G. Binnig, " Cognition network technology for a fully automated 3D segmentation of liver.," in MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: A Grand Challenge (2007), pp. 125-134.
41. K. A. Saddi, M. Rousson, C. C. hotel, and F. Cheriet, Global-to-local shape matching for liver segmentation in CT imaging. in MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic: A Grand Challenge (2007), pp. 207-214.
42. W. N. J. W. Yussof and H. Burkhardt, " Automatic 3D liver segmentation using morphological operations and graph-cut techniques.," Journal of Next Generation Information Technology 2 (3), 23-34 (2011). 10.4156/jnit.vol2.issue3.2
43. A. S. Maklad, M. Matsuhiro, H. Suzuki, Y. Kawata, N. Niki, T. Utsunomiya, and M. Shimada, " Blood vessel-based liver segmentation through the portal phase of a CT dataset.," Proc. SPIE 8670, 86700X-1-86700X-7 (2013) 10.1117/12.2007546
44. A. S. Maklad, M. Matsuhiro, H. Suzuki, Y. Kawata, N. Niki, T. Utsunomiya, and M. Shimada, " Extraction of liver volumetry based on blood vessel from the portal phase CT dataset.," Proc. SPIE 8314, 83142O-1-83142O-7 (2012). 10.1117/12.911175
45. T. Saitoh, Y. Tamura, and T. Kaneko, " Automatic segmentation of liver region based on extracted blood vessels.," Syst. Comput. Jpn. 35 (5), 1-10 (2004) [Denshi Joho Tsushin Gakkai Ronbunshi J86-D-II(5), 633-641 (2003)]. 10.1002/scj.10592
46. I. R. Francis, R. H. Cohan, N. J. McNulty, J. F. Platt, M. Korobkin, A. Gebremariam, and K. I. Ragupathi, " Multidetector CT of the liver and hepatic neoplasms: Effect of multiphasic imaging on tumor conspicuity and vascular enhancement.," AJR, Am. J. Roentgenol. 180 (5), 1217-1224 (2003). 10.2214/ajr.180.5.1801217
47. A. Oto, K. Kulkarni, R. Nishikawa, and R. L. Baron, " Contrast enhancement of hepatic heangiomas on multiphase MDCT: Can we daiagnose hepatic hemangiomas by comparing enhancement with blood pool?," AJR, Am. J. Roentgenol. 195 (2), 381-386 (2010). 10.2214/AJR.09.3324
48. B. V. Ginnekan, T. Heimann, and M. Styner, " 3D Segmentation in the clinic: A grand challenge.," in MICCAI 2007 Workshop Proceedings of the 3D Segmentation in the Clinic:A Grand Challenge (American Roentgen Ray Society, 2007), pp 7-15.
49. I. Pitas, Digital Image Processing Algorithms and Applications (Wily Inter Science, U.S.A., 2000).
50. A. Fabijanska " Results of applying a two-path region growing algorithm for airway tree segmentation to MDCT chest scans from EXACT database.," EXACT09 (2009), pp. 251-260 (available URL: http://image.diku.dk/exact/result/VOLCED-description.pdf), accessed 2 July 2013.
51. S. W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing, 2nd ed., (California Technical Publishing, San Diego, 1997 -2011), Chap., pp 436-442 (available URL: http://www.dspguide.com/CH25.PDF), accessed 9 October 2012.
52. J. Toriwaki and H. Yoshida, Fundamentals of Three-Dimensional Digital Image Processing (Springer-Verlag London Limited, 2009), Sec. 5.
53. E. Takahashi, Y. Kawata, N. Niki, Y. Nakano, M. Harada, and N. Moriyama, " Computer aided diagnosis for osteoporosis based on vertebral column structure analysis.," Proc. SPIE 8315, 831533 (2012). 10.1117/12.911660
54. D. F. Watson, " Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes.," Comput. J. 24 (2), 167-172 (1981). 10.1093/comjnl/24.2.167
55. K. Palágyi, J. Tschirren, E. A. Hoffman, and M. Sonka, " Quantitative analysis of pulmonary airway tree structures.," Comput. Biol. Med. 36 (9), 974-996 (2006). 10.1016/j.compbiomed.2005.05.004
56. C. M. Bishop, Pattern Recognition and Machine Learning (Springer Science+Business Media, LLC, Singapore), Sec. 10.
57. S. O'Brien, " Computing the Solid Angle of Two Detectors Using Monte Carlo Methods. " (available URL: http://acadine.physics.jmu.edu/group/ technical-notes/Solid-angle-latex/solid-angle.pdf), accessed 18 December 2012.
58. P. Dalgaard, Statistics and Computing: Introductory Statistics with R (Springer-Verlag, New York, 2002).