Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm

  • Apollon ZygomalasEmail author
  • Dionissios Karavias
  • Dimitrios Koutsouris
  • Ioannis Maroulis
  • Dimitrios D. Karavias
  • Konstantinos Giokas
  • Vasileios Megalooikonomou
Original Article


We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013–July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance.


Computer-assisted surgery Liver segmentation Hepatectomy Liver neoplasms Liver tumor 



This study was supported by a grant for Ph.D. studies from the Onassis Foundation. Thanks are due to Dr. G. Drosou (Biologist) for her indications on 3D visualization. Thanks are due to Mrs. A. Skoura (Computer Engineer) for her help in analysis of anatomical tree-shape structures.


This study was supported by a grant for Ph.D. studies from the Onassis Foundation.

Compliance with ethical standards

Conflict of interest

Author ZA has received research grants from the Onassis Foundation. All the other 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 1964 Helsinki declaration and its later amendments or comparable ethical standards.


  1. 1.
    Abdalla EK, Denys A, Chevalier P et al (2004) Total and segmental liver volume variations: implications for liver surgery. Surgery 135:404–410. doi: 10.1016/j.surg.2003.08.024 CrossRefPubMedGoogle Scholar
  2. 2.
    Babalola KO, Patenaude B, Aljabar P et al (2009) An evaluation of four automatic methods of segmenting the subcortical structures in the brain. NeuroImage 47:1435–1447. doi: 10.1016/j.neuroimage.2009.05.029 CrossRefPubMedGoogle Scholar
  3. 3.
    Center for Integrative Biomedical Computing (CIBC) (2015) Seg3D: volumetric image segmentation and visualization. Scientific computing and imaging institute (SCI). Accessed 5 Jan 2015
  4. 4.
    Dawant BM, Li R, Lennon B, Li S (2007) Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 215–221Google Scholar
  5. 5.
    Dello SAWG, van Dam RM, Slangen JJG et al (2007) Liver volumetry plug and play: do it yourself with ImageJ. World J Surg 31:2215–2221. doi: 10.1007/s00268-007-9197-x CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    DuBray BJ Jr, Levy RV, Balachandran P et al (2011) Novel three-dimensional imaging technique improves the accuracy of hepatic volumetric assessment. HPB 13:670–674. doi: 10.1111/j.1477-2574.2011.00350.x CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341. doi: 10.1016/j.mri.2012.05.001 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Grundmann RT, Hermanek P, Merkel S et al (2008) Arbeitsgruppe Workflow Diagnostik und Therapie von Lebermetastasen kolorektaler Karzinome. Diagnosis and treatment of colorectal liver metastases—workflow. Zentralblatt Für Chir 133:267–284. doi: 10.1055/s-2008-1076796 CrossRefGoogle Scholar
  9. 9.
    Heimann T, van Ginneken B, Styner MA et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251–1265. doi: 10.1109/TMI.2009.2013851 CrossRefPubMedGoogle Scholar
  10. 10.
    Kohlberger T, Singh V, Alvino C et al (2012) Evaluating segmentation error without ground truth. In: Ayache N, Delingette H, Golland P, Mori K (eds) Medical image computing and computer assisted intervention—MICCAI 2012. Springer, Berlin, pp 528–536CrossRefGoogle Scholar
  11. 11.
    MATLAB—the language of technical computing—A. Accessed 6 Nov 2014
  12. 12.
    Ohshima S (2014) Volume analyzer SYNAPSE VINCENT for liver analysis. J Hepato-Biliary-Pancreat Sci 21:235–238. doi: 10.1002/jhbp.81 CrossRefGoogle Scholar
  13. 13.
    Simpson AL, Dumpuri P, Jarnagin WR, Miga MI (2012) Model-assisted image-guided liver surgery using sparse intraoperative data. In: Payan Y (ed) Soft tissue biomechanical modeling computer assisted surgery. Springer, Berlin, pp 7–40CrossRefGoogle Scholar
  14. 14.
    SLIVER07: home. Accessed 27 Jan 2015
  15. 15.
    Soler L, Nicolau S, Pessaux P et al (2014) Real-time 3D image reconstruction guidance in liver resection surgery. Hepatobiliary Surg Nutr 3:73–81. doi: 10.3978/j.issn.2304-3881.2014.02.03 PubMedPubMedCentralGoogle Scholar
  16. 16.
    Takamoto T, Hashimoto T, Ogata S et al (2013) Planning of anatomical liver segmentectomy and subsegmentectomy with 3-dimensional simulation software. Am J Surg 206:530–538. doi: 10.1016/j.amjsurg.2013.01.041 CrossRefPubMedGoogle Scholar
  17. 17.
    Van der Vorst JR, van Dam RM, van Stiphout RSA et al (2010) Virtual liver resection and volumetric analysis of the future liver remnant using open source image processing software. World J Surg 34:2426–2433. doi: 10.1007/s00268-010-0663-5 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Vauthey JN, Chaoui A, Do KA et al (2000) Standardized measurement of the future liver remnant prior to extended liver resection: methodology and clinical associations. Surgery 127:512–519. doi: 10.1067/msy.2000.105294 CrossRefPubMedGoogle Scholar
  19. 19.
    Yang X, Yu HC, Choi Y et al (2014) A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput Methods Programs Biomed 113:69–79. doi: 10.1016/j.cmpb.2013.08.019 CrossRefPubMedGoogle Scholar
  20. 20.
    Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31:1116–1128. doi: 10.1016/j.neuroimage.2006.01.015 CrossRefPubMedGoogle Scholar
  21. 21.
    Zahel T, Wildgruber M, Ardon R et al (2013) Rapid assessment of liver volumetry by a novel automated segmentation algorithm. J Comput Assist Tomogr 37:577–582. doi: 10.1097/RCT.0b013e31828f0baa CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • Apollon Zygomalas
    • 1
    • 3
    Email author
  • Dionissios Karavias
    • 1
  • Dimitrios Koutsouris
    • 2
  • Ioannis Maroulis
    • 1
  • Dimitrios D. Karavias
    • 1
  • Konstantinos Giokas
    • 2
  • Vasileios Megalooikonomou
    • 3
  1. 1.Hepatobiliary and Pancreatic Unit, Department of SurgeryUniversity Hospital of PatrasPatrasGreece
  2. 2.Biomedical Engineering Laboratory, School of Electrical and Computer EngineeringNational Technical University of AthensZografou, AthensGreece
  3. 3.Computer Engineering and Informatics Department, School of EngineeringUniversity of PatrasRio, PatrasGreece

Personalised recommendations