Automated liver segmentation from a postmortem CT scan based on a statistical shape model

  • Atsushi SaitoEmail author
  • Seiji Yamamoto
  • Shigeru Nawano
  • Akinobu Shimizu
Original Article



Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver.


The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation–maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label.


The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference.


We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.


Liver segmentation Postmortem CT Statistical shape model EM algorithm Autopsy imaging Synthesized-based learning 



This work was supported in part by JSPS KAKENHI Grant Number 15J08775 and MEXT KAKENHI Grant Number 26108002.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki declaration, as revised in 2008(5).

Informed consent

For this type of study, formal consent is not required.


  1. 1.
    Ezawa H, Yoneyama R, Kandatsu S, Yoshikawa K, Tsujii H, Harigaya K (2003) Introduction of autopsy imaging redefines the concept of autopsy: 37 cases of clinical experience. Pathol Int 53(12):865–873. doi: 10.1046/j.1440-1827.2003.01573.x PubMedCrossRefGoogle Scholar
  2. 2.
    Okuda T, Shiotani S, Sakamoto N, Kobayashi T (2013) Background and current status of postmortem imaging in Japan: short history of “autopsy imaging (Ai)”. Forensic Sci Int 225(1):3–8. doi: 10.1016/j.forsciint.2012.03.010 PubMedCrossRefGoogle Scholar
  3. 3.
    Saito A, Shimizu A, Watanabe H, Yamamoto S, Kobatake H (2013) Automated liver segmentation from a CT volume of a cadaver using a statistical shape model. Int J Comput Assist Radiol Surg 8(Suppl 1):S48–S49. doi: 10.1007/s11548-013-0850-6 Google Scholar
  4. 4.
    Punia R, Singh S (2013) Review on machine learning techniques for automatic segmentation of liver images. Int J Adv Res Comput Sci Softw Eng 3(4):666–670Google Scholar
  5. 5.
    Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492. doi: 10.1109/TMI.2003.809139 PubMedCrossRefGoogle Scholar
  6. 6.
    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):135–142. doi: 10.1007/s11548-007-0135-z CrossRefGoogle Scholar
  7. 7.
    Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y, Nimura Y, Rueckert D, Mori K (2013) Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. In: Medical image computing and computer-assisted intervention. Springer, pp 165–172. doi: 10.1007/978-3-642-40763-5_21
  8. 8.
    Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D (2013) Automated abdom inal multi-organ segmentation with subject-specific atlas generation. IEEE Trans Med Imaging 32(9):1723–1730. doi: 10.1109/TMI.2013.2265805 PubMedCrossRefGoogle Scholar
  9. 9.
    Umetsu S, Shimizu A, Watanabe H, Kobatake H, Nawano S (2014) An automated segmentation algorithm for CT volumes of livers with atypical shapes and large pathological lesions. IEICE Trans Inf Syst 97(4):951–963. doi: 10.1587/transinf.E97.D.951 CrossRefGoogle Scholar
  10. 10.
    Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal 23(1):92–104. doi: 10.1016/ PubMedCrossRefGoogle Scholar
  11. 11.
    Kainmüller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proceedings of MICCAI Workshop 3D segmentation in the clinic: a grand challenge, pp 109–116Google Scholar
  12. 12.
    Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu DS, Rau AM, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265. doi: 10.1109/TMI.2009.2013851 PubMedCrossRefGoogle Scholar
  13. 13.
    Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563. doi: 10.1016/ PubMedCrossRefGoogle Scholar
  14. 14.
    Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59. doi: 10.1006/cviu.1995.1004 CrossRefGoogle Scholar
  15. 15.
    Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215. doi: 10.1007/s11263-006-8711-1 CrossRefGoogle Scholar
  16. 16.
    Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239. doi: 10.1109/34.969114 CrossRefGoogle Scholar
  17. 17.
    Tomoshige S, Oost E, Shimizu A, Watanabe H, Nawano S (2014) A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images. Med Image Anal 18(1):130–143. doi: 10.1016/ PubMedCrossRefGoogle Scholar
  18. 18.
    Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal 26(1):1–18. doi: 10.1016/ PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Linguraru MG, Pura JA, Pamulapati V, Summers RM (2012) Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT. Med Image Anal 16(4):904–914. doi: 10.1016/ PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Saito A, Shimizu A, Watanabe H, Yamamoto S, Nawano S, Kobatake H (2014) Statistical shape model of a liver for autopsy imaging. Int J Comput Assist Radiol Surg 9(2):269–281. doi: 10.1007/s11548-013-0923-6 PubMedCrossRefGoogle Scholar
  21. 21.
    Cremers D (2006) Dynamical statistical shape priors for level set-based tracking. IEEE Trans Pattern Anal Mach Intell 28(8):1262–1273. doi: 10.1109/TPAMI.2006.161 PubMedCrossRefGoogle Scholar
  22. 22.
    Uchida Y, Shimizu A, Kobatake H, Nawano S, Shinozaki K (2010) A comparative study of statistical shape models of the pancreas. Int J Comput Assist Radiol Surg 5(Suppl 1):S385–S387. doi: 10.1007/s11548-010-0469-9 Google Scholar
  23. 23.
    Pohl KM, Fisher J, Bouix S, Shenton M, McCarley RW, Grimson WEL, Kikinis R, Wells WM (2007) Using the logarithm of odds to define a vector space on probabilistic atlases. Med Image Anal 11(5):465–477. doi: 10.1016/ PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Sanjay-Gopal S, Hebert T (1998) Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm. IEEE Trans Image Process 7(7):1014–1028. doi: 10.1109/83.701161 PubMedCrossRefGoogle Scholar
  25. 25.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (methodol). doi: 10.2307/2984875 Google Scholar
  26. 26.
    Shimizu A, Nakagomi K, Narihira T, Kobatake H, Nawano S, Shinozaki K, Ishizu K, Togashi K (2010) Automated segmentation of 3D CT images based on statistical atlas and graph cuts. In: Medical computer vision. Recognition techniques and applications in medical imaging. Springer, pp 214–223. doi: 10.1007/978-3-642-18421-5_21
  27. 27.
    Saito T, Toriwaki JI (1994) New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognit 27(11):1551–1565. doi: 10.1016/0031-3203(94)90133-3 CrossRefGoogle Scholar
  28. 28.
    Maurer CR Jr, Qi R, Raghavan V (2003) A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell 25(2):265–270. doi: 10.1109/TPAMI.2003.1177156 CrossRefGoogle Scholar
  29. 29.
    Demšar J (2006) Statistical comparisons of classifiers over multiple data. J Mach Learn Res 7:1–30Google Scholar
  30. 30.
    Hasegawa I, Shimizu A, Saito A, Püschel K, Suzuki H, Vogel H, Heinemann A (2016) Evaluation of post-mortem lateral cerebral ventricle changes using sequential scans dur-ing post-mortem computed tomography. Int J Legal Med. doi: 10.1007/s00414-016-1327-2 PubMedPubMedCentralGoogle Scholar
  31. 31.
    Saito A, Nawano S, Shimizu A (2016) Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs. Med Image Anal 28:46–65. doi: 10.1016/ PubMedCrossRefGoogle Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  • Atsushi Saito
    • 1
    Email author
  • Seiji Yamamoto
    • 2
  • Shigeru Nawano
    • 3
  • Akinobu Shimizu
    • 1
  1. 1.Tokyo University of Agriculture and TechnologyKoganeiJapan
  2. 2.Ai Information CenterChuo-kuJapan
  3. 3.Department of RadiologyInternational University of Health and Welfare, Mita HospitalMinato-kuJapan

Personalised recommendations