Tumor burden analysis on computed tomography by automated liver and tumor segmentation

IEEE Transactions on Medical Imaging

Volumn 31, Issue 10, 2012, Pages 1965-1976

  Linguraru, Marius George ^a,b   Richbourg, William J ^b   Liu, Jianfei ^b   Watt, Jeremy M ^b   Pamulapati, Vivek ^b   Wang, Shijun ^b   Summers, Ronald M ^b  

^a CHILDREN'S NATIONAL MEDICAL CENTER   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Cancer; contrast enhanced computed tomography (CT); liver; parameterization; segmentation; shape; tumor burden

Indexed keywords

AFFINE INVARIANT; AUTOMATED SEGMENTATION; CANCER; COMPUTED TOMOGRAPHY; CONCAVE SURFACE; FALSE POSITIVE; GEODESIC ACTIVE CONTOUR; GRAPH CUT; HEPATIC TUMORS; INITIAL SEGMENTATION; LIVER SEGMENTATION; LOCAL SHAPE; SHAPE; SHAPE PARAMETERIZATION; TEST DATA; TRAINING SETS; TUMOR DETECTION; TUMOR SEGMENTATION;

AUTOMATION; DISEASES; IMAGE SEGMENTATION; LIVER; PARAMETERIZATION; TUMORS;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; FACTUAL DATABASE; HUMAN; LIVER; LIVER TUMOR; MALE; METHODOLOGY; PATHOLOGY; PROSTATE TUMOR; RADIOGRAPHY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING; TUMOR VOLUME;

DATABASES, FACTUAL; HUMANS; IMAGING, THREE-DIMENSIONAL; LIVER; LIVER NEOPLASMS; MALE; PROSTATIC NEOPLASMS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES; TOMOGRAPHY, X-RAY COMPUTED; TUMOR BURDEN;

EID: 84867082042     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2012.2211887     Document Type: Article

References (66)

1. American Cancer Society, Cancer Facts and Figures 2011. Atlanta, GA: Am. Cancer Soc., 2011.
2. D. H. Ballard and C. M. Brown, Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1981.
3. A. Beck and V. Aurich, "HepaTux-A semiautomatic liver segmentation system," inMICCAIWorkshop on 3-D Segmentation in the Clinic: A Grand Challenge, 2007.
4. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/max-flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124-1137, Sep. 2004.
5. D. C. Broering, M. Sterneck, and X. Rogiers, "Living donor liver transplantation," J. Hepatol., vol. 38, pp. S119-S135, 2003.
6. C. Brechbuhler, G. Gerig, and O. Kubler, "Parameterization of closed surfaces for 3-D shape descriptor," Comput. Vis. Image Understand., vol. 61, no. 2, pp. 154-170, 1995.
7. P. Campadelli, E. Casiraghi, and A. Esposito, "Liver segmentation from computed tomography scans: A survey and a new algorithm," Artif. Intell. Med., vol. 45, pp. 185-196, 2009.
8. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," Int. J. Comput. Vis., vol. 22, no. 1, pp. 61-97, 1997.
9. C. Coghlin and G. I. Murray, "Current and emerging concepts in tumour metastasis," J. Pathol., vol. 222, no. 1, pp. 1-15, 2010.
10. B. M. Dawant et al., "Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set," in MICCAI Workshop 3-D Segmentation in the Clinic: A Grand Challenge, 2007.
11. X. Deng and G. Du, "Editorial: 3-D segmentation in the clinic: A grand challenge II-Liver tumor segmentation," in MICCAIWorkshop, 2008.
12. E. A. Eisenhauer et al., "New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)," Eur. J. Cancer, vol. 45, pp. 228-247, 2009.
13. J. Ellert and L. Kreel, "The role of computed tomography in the initial staging and subsequent management of the lymphomas," J. Comput. Assist. Tomogr., vol. 4, no. 3, pp. 368-391, 1980. (Pubitemid 10113985)
14. M. S. Floater and K. Hormann, Surface Parameterization: A Tutorial and Survey [Advances in Multiresolution for GeometricModelling]. New York: Springer, 2005, pp. 157-186.
15. M. Freiman, O. Cooper, D. Lischinski, and L. Joskowicz, "Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation," Int. J. Comput. Assist. Radiol. Surg., vol. 6, no. 2, pp. 247-255, 2011.
16. G. Gerig and M. Styner, "Shape versus size: Improved understanding of the morphology of brain structures," Medical Image Computing and Computer Assisted Intervention, vol. 2208, LNCS, pp. 24-32, 2001. (Pubitemid 33352393)
17. P. Gobbi et al., "The clinical value of tumor burden at diagnosis in Hodgkin Lymphoma," Cancer, vol. 101, no. 8, pp. 1824-1834, 2004. (Pubitemid 39314746)
18. C. F. Gonsalves et al., "Radioembolization as salvage therapy for hepatic metastasis of uveal melanoma: A single-institution experience," Am. J. Roentgenol., vol. 196, no. 2, pp. 468-473, 2011.
19. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man Cybern., vol. 3, no. 6, pp. 610-621, Nov. 1973.
20. Y. Häme and M. Pollari, "Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation," Med. Image Anal., 2011, to be published.
21. T. Heimann and H. P. Meinzer, "Statistical shape models for 3-D medical image segmentation: A review," Med. Image Anal., vol. 13, no. 4, pp. 543-563, 2009.
22. T. Heimann et al., "Comparison and evaluation of methods for liver segmentation from CT datasets," IEEE Trans. Med. Imag., vol. 28, no. 8, pp. 1251-1265, Aug. 2009.
23. B. Hopenfeld, H. Ashikaga, and E. R. McVeigh, "Geodesic based registration of sensor data and anatomical surface image data," Ann. Biomed. Eng., vol. 35, no. 10, pp. 1771-1781, 2007. (Pubitemid 47444147)
24. K. D. Hopper, K. Singapuri, and A. Finkel, "Body CT and oncologic imaging," Radiology, vol. 215, pp. 27-40, 2000. (Pubitemid 30190507)
25. H. Huang et al., "Surface alignment of 3-D spherical harmonic models: Application to cardiac MRI analysis," MICCAI, vol. 3749, LNCS, pp. 67-74, 2005. (Pubitemid 43828235)
26. L. Ibanez, W. Schroeder, L.Ng, and J. Cates, The ITKSoftware Guide Second Edition 2005 [Online]. Available: http://www.itk.org
27. S. Jagannath et al., "Tumor burden assessment and its implication for a prognostic model in advanced diffuse large-cell lymphoma," J. Clin. Oncol., vol. 4, no. 6, pp. 859-865, 1986. (Pubitemid 16080938)
28. D. Kainmuller, T. Lange, and H. Lamecker, "Shape constrained automatic segmentation of the liver based on a heuristic intensity model," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge, 2007.
29. S. Kawasaki et al., "Preoperative measurement of segmental liver volume of donors for living related liver transplantation," Hepatology, vol. 18, no. 5, pp. 1115-1120, 1993. (Pubitemid 23320441)
30. V. Kolmogorov and Y. Boykov, "What metrics can be approximated by geo-cuts, or global optimization of length/area and flux," in IEEE Int. Conf. Comput. Vis., 2005, pp. 564-571. (Pubitemid 44055006)
31. H. Lamecker, T. Lange, andM. Seebass, "A statistical shape model for the liver," MICCAI, vol. 2489, LNCS, pp. 412-427, 2002.
32. R. L. Lewis, Liver and Biliary Tract Tumors, L. Goldman and D. Ausiello, Eds., 23rd ed. Philadelphia, PA: Saunders Elsevier, 2007, ch. 206.
33. H. Ling et al., "Hierarchical, learning-based automatic liver segmentation," in 26th IEEE Conf. Comput. Vis. Pattern Recognit., Anchorage, AK, 2008.
34. M. G. Linguraru et al., "Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation,"Med. Phys., vol. 37, no. 2, pp. 771-783, 2010.
35. M. G. Linguraru et al., "Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization," in MICCAI 2010, New York, 2010, vol. 6363, LNCS, pp. 89-96.
36. J. Liu, W. Gao, S. Huang, andW. L. Nowinski, "A model-based, semiglobal segmentation approach for automatic 3-D point landmark localization in neuroimages," IEEE Trans. Med. Imag., vol. 27, no. 8, pp. 1034-1044, Aug. 2008.
37. S. Manay et al., "Integral invariants for shape matching," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 10, pp. 1602-1620, Oct. 2006.
38. L.Massoptier and S. Casciaro, "A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans," Eur. Radiol., vol. 18, no. 8, pp. 1658-1665, 2008.
39. C. E. Metz, B. A. Herman, and C. A. Roe, "Statistical comparison of two ROC curve estimates obtained from partially-paired datasets," Med. Decision Making, vol. 18, pp. 110-121, 1998. (Pubitemid 28041647)
40. J. Moltz, L. Bornemann, V. Dicken, and H. Peitgen, "Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing," inMICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge II, 2008.
41. M. A. Muller, B. Marincek, and I. Frauenfelder, "State of the art 3-D imaging of abdominal organs," JBR-BTR, vol. 90, pp. 467-474, 2007. (Pubitemid 351302436)
42. T. Okada et al., "Automated segmentation of the liver from 3-D CT images using probabilistic atlas andmultilevel statistical shapemodel," Acad. Radiol., vol. 15, no. 11, pp. 1390-1403, 2008.
43. N. Paragios, M. Rousson, and V. Ramesh, "Distance functions for non-rigid registration," Comput. Vis. Image Understand., pp. 142-165, 2003.
44. H. C. Peng, F. H. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy," IEEE Trans. Pattern Anal.Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005. (Pubitemid 41245053)
45. A. Peterhans et al., "A navigation system for open liver surgery: Design, workflow and first clinical applications," Int. J. Med. Robot. Comput. Assist. Surg., 2010.
46. S. R. Prasad et al., "CT tumor measurement for therapeutic response assessment: Comparison of unidimensional, bidimensional, and volumetric techniques-Initial Observations1," Radiology, vol. 225, no. 2, pp. 416-419, 2002. (Pubitemid 35239241)
47. L. Rusko et al., "Fully automatic liver segmentation for contrast-enhanced CT images," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge, 2007.
48. Y. Sato et al., 3-D Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images, ser. Lecture Notes in Computer Science. New York: Springer, 1997, vol. 1205, pp. 213-222. (Pubitemid 127045554)
49. G. Schmidt et al., "Cognition network technology for a fully automated 3-D segmentation of the liver," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge, 2007.
50. G. Schmidt, G. Binnig,M. Kietzmann, and J. Kim, "Cognition network technology for a fully automated 3-D segmentation of liver tumors," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge II, 2008.
51. D. Seghers et al., "Model-based segmentation using graph representations," in Med. Image Comput. Comput. Assist. Interv. Int. Conf. Med. Image Comput. Comput. Assist. Interv, 2008, vol. 11, pp. 393-400.
52. D. Selle, B. Preim, A. Schenk, and H. O. Peitgen, "Analysis of vasculature for liver surgical planning," IEEE Trans. Med. Imag., vol. 22, no. 11, pp. 1344-1357, Nov. 2002.
53. J. A. Sethian, Level Set Methods and Fast Marching Methods. Cambridge, U.K.: Cambridge Univ. Press, 1999.
54. N. Shevchenko et al., "MiMed liver: A planning system for liver surgery," IEEE Annu. Conf. Eng. Med. Biol. Soc., pp. 1882-1885, 2010.
55. A. Shimizu et al., "Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge II, 2008.
56. D. Smeets, B. Stijnen, D. Loeckx, B. Dobbelaer, and P. Suetens, "Segmentation of liver metastases using a level set method with spiral-scanning technique and supervised fuzzy pixel classification," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge II, 2008.
57. L. Soler et al., "Fully automatic anatomical, pathological, and functional segmentation fromCT scans for hepatic surgery," Comput. Aided Surg., vol. 6, no. 3, pp. 131-142, 2001. (Pubitemid 33111133)
58. P. Soyer et al., "Hepaticmetastases from colorectal cancer: Influence of hepatic volumetric analysis on surgical decision making," Radiology, vol. 184, no. 3, pp. 695-697, 1992.
59. J. Stawiaski, E. Decenciere, and F. Bidault, "Interactive liver tumor segmentation using graph-cuts and watershed," in MICCAI Workshop on 3-D Segmentation in the Clinic: A Grand Challenge II, 2008.
60. M. Styner, A. J. Lieberman, D. Pantazis, and G. Gerig, "Boundary and medial shape analysis of the hippocampus in schizophrenia," Med. Imag. Anal., vol. 8, no. 3, pp. 97-203, 2004.
61. Y. Tsushima and K. Endo, "Spleen enlargement in patients with nonalcoholic fatty liver: Correlation between degree of fatty infiltration in liver and size of spleen," Dig. Dis. Sci., vol. 45, no. 1, pp. 196-200, 2000. (Pubitemid 30111709)
62. J.Watt, M. G. Linguraru, and R.M. Summers, "Affine invariant shape parameterization to assess local 3-D shape in abdominal organs," SPIE Med. Imag., pp. 79650-1-79650-8, 2011.
63. A.Wimmer, G. Soza, and J. Hornegger, "A generic probabilistic active shape model for organ segmentation," Med. Image Comput. Comput. Assist. Intervent., vol. 12, no. 2, pp. 26-33, 2009.
64. J. W. Xu and K. Suzuki, "Computer-aided detection of hepatocellular carcinoma in hepatic CC: False positive reduction with feature selection," in IEEE Int. Symp. Biomed. Imag., 2011, pp. 1097-1100.
65. Z. Xue, D. Shen, and C. Davatzikos, "Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels," IEEE Trans. Med. Imag., vol. 23, no. 10, pp. 1276-1291, Oct. 2004.
66. J. Zhou et al., "Semi-automatic segmentation of 3-D liver tumors from CT scans using voxel classification and propagational learning," in MICCAI Workshop on 3-D Segmentat. Clinic: A Grand Challenge II, 2008.