Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images

  • Jinke Wang
  • Yuanzhi Cheng
  • Changyong Guo
  • Yadong Wang
  • Shinichi Tamura
Original Article



Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images.


First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape–intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms.


Using the 25 test CT datasets, average symmetric surface distance is \(1.09 \pm 0.34\) mm (range 0.62–2.12 mm), root mean square symmetric surface distance error is \(1.72 \pm 0.46\) mm (range 0.97–3.01 mm), and maximum symmetric surface distance error is \(18.04 \pm 3.51\) mm (range 12.73–26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques.


The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.


Active shape model Statistical shape model Expectation maximization Atlas-based segmentation Level set segmentation 



The authors would like to thank the anonymous reviewers for their valuable comments and help suggestions that greatly improved the papers quality. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61571158; the Scientific Research Fund of Heilongjiang Provincial Education Department (No. 12541164), and the Nature Science Foundation of Heilongjiang Province of China (No. F2015005).

Compliance with ethical standards

Conflict of interest

There is no conflict of interest in this study.


  1. 1.
    Meinzer HP, Thorn M, Crdenas CE (2002) Computerized planning of liver surgery—an overview. Comput Graph 26(4):569–576CrossRefGoogle Scholar
  2. 2.
    Masumoto J, Hori M, Sato Y, Murakami T, Johkoh T, Nakamura H, Tamura S (2003) Automated liver segmentation using multislice CT images. Syst Comput 34(9):71–82CrossRefGoogle Scholar
  3. 3.
    Shiffman S, Rubin GD, Napel S (2000) Medical image segmentation using analysis of isolable-contour maps. IEEE Trans Med Imaging 19(11):1064–1074CrossRefPubMedGoogle Scholar
  4. 4.
    Bae KT, Giger ML, Chen CT, Kahn CE Jr (1993) Automatic segmentation of liver structure in CT images. Med Phys 20(1):71–78CrossRefPubMedGoogle Scholar
  5. 5.
    Ruskó L, Bekes G, Fidrich M (2009) Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images. Med Image Anal 13(6):871–882CrossRefPubMedGoogle Scholar
  6. 6.
    Selver MA, Kocaoǧlu A, Demir GK, Doanǧ H, Dicle O, Güzeliş C (2008) Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation. Comput Biol Med 38(7):765–784CrossRefPubMedGoogle Scholar
  7. 7.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefPubMedGoogle Scholar
  8. 8.
    Okada T, Shimada R, Hori M, Nakamoto M, Chen Y-W, Nakamura H, Sato Y (2008) Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. Acad Radiol 15(11):1390–1403CrossRefPubMedGoogle Scholar
  9. 9.
    So R, Chung A (2009) Multi-level non-rigid image registration using graph-cuts. In: IEEE international conference on acoustics, speech and signal processing, 2009. ICASSP 2009. IEEE, pp 397–400Google Scholar
  10. 10.
    Wimmer A, Soza G, Hornegger J (2009) A generic probabilistic active shape model for organ segmentation. In: MICCAI. Springer, pp 26–33Google Scholar
  11. 11.
    Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y, Wolz R, Rueckert D, Mori K (2013) Multi-organ segmentation from 3D abdominal CT images using patient-specific weighted-probabilistic atlas. In: SPIE medical imaging. International Society for Optics and Photonics, pp 86693–86697Google Scholar
  12. 12.
    Linguraru MG, Sandberg JK, Li Z, Pura JA, Summers RM (2009) Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation. In: MICCAI. Springer, pp 1001–1008Google Scholar
  13. 13.
    Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59CrossRefGoogle Scholar
  14. 14.
    Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492CrossRefPubMedGoogle Scholar
  15. 15.
    Okada T, Yokota K, Hori M, Nakamoto M, Nakamura H, Sato Y (2008) Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In: MICCAI. Springer, pp 502–509Google Scholar
  16. 16.
    Oda M, Nakaoka T, Kitasaka T, Furukawa K, Misawa K, Fujiwara M, Mori K (2012) Organ segmentation from 3D abdominal CT images based on atlas selection and graph cut. In: Abdominal Imaging. Computational and clinical applications. Springer, pp 181–188Google Scholar
  17. 17.
    Shimizu A, Ohno R, Ikegami T, Kobatake H, Nawano S, Smutek D (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surg 2(3–4):135–142CrossRefGoogle Scholar
  18. 18.
    Wolz R, Chu C, Misawa K, Mori K, Rueckert D (2012) Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases. In: MICCAI. Springer, pp 10–17Google Scholar
  19. 19.
    Oliveira DA, Feitosa RQ, Correia MM (2011) Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed Eng Online 10(1):30CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Yang J, Duncan JS (2004) 3D image segmentation of deformable objects with joint shape–intensity prior models using level sets. Med Image Anal 8(3):285–294CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Li BN, Chui CK, Chang S, Ong SH (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput in Biol Med 41(1):1–10CrossRefGoogle Scholar
  22. 22.
    Linguraru MG, Richbourg WJ, Watt JM, Pamulapati V, Summers RM (2012) Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts. In: Abdominal imaging. Computational and clinical applications. Springer, pp 198–206Google Scholar
  23. 23.
    Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  24. 24.
    Van Ginneken B, Heimann T, Styner M (2007) 3D segmentation in the clinic: a grand challenge. 3D segmentation in the clinic: a grand challenge, pp 7–15Google Scholar
  25. 25.
    Chi Y, Zhou J, Venkatesh SK, Huang S, Tian Q, Hennedige T, Liu J (2013) Computer-aided focal liver lesion detection. Int J Comput Assist Radiol Surg 8(4):511–525CrossRefPubMedGoogle Scholar
  26. 26.
    Heimann T, Van Ginneken B, Styner M et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265CrossRefPubMedGoogle Scholar
  27. 27.
    Zhou X, Kitagawa T, Hara T, Fujita H, Zhang X, Yokoyama R, Kondo H, Kanematsu M, Hoshi H (2006) Constructing a probabilistic model for automated liver region segmentation using non-contrast X-ray torso CT images. In: MICCAI. Springer, pp 856–863Google Scholar
  28. 28.
    Li C, Wang X, Li J, Eberl S, Fulham M, Yin Y, Feng DD (2013) Joint probabilistic model of shape and intensity for multiple abdominal organ segmentation from volumetric CT images. IEEE Trans Inf Technol Biomed 17(1):92–102Google Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Jinke Wang
    • 1
    • 2
  • Yuanzhi Cheng
    • 1
  • Changyong Guo
    • 1
  • Yadong Wang
    • 1
  • Shinichi Tamura
    • 3
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Software EngineeringHarbin University of Science and TechnologyRongchengChina
  3. 3.Center for Advanced Medical Engineering and InformaticsOsaka UniversitySuitaJapan

Personalised recommendations