Skip to main content
Log in

A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans

  • Computer Applications
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and/or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions from CT scans. No interaction between the user and analysis system is required for initialization since the algorithm is fully automatic. A statistical model-based approach was created to distinguish hepatic tissue from other abdominal organs. It was combined to an active contour technique using gradient vector flow in order to obtain a smoother and more natural liver surface segmentation. Thereafter, automatic classification was performed to isolate hepatic lesions from liver parenchyma. Twenty-one datasets, presenting different anatomical and pathological situations, have been processed and analyzed. Special focus has been driven to the resulting processing time together with quality assessment. Our method allowed robust and efficient liver and lesion segmentations very close to the ground truth, in a relatively short processing time (average of 11.4 s for a 512 × 512-pixel slice). A volume overlap of 94.2% and an accuracy of 3.7 mm were achieved for liver surface segmentation. Sensitivity and specificity for tumor lesion detection were 82.6% and 87.5%, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. World Health Organization (2002) The World Health Report 2002. Available online at: http://www.who.int/whr/2002/en. Accessed May 9, 2007

  2. Foley WD (2005) Liver: surgical planning. Eur Radiol 15(suppl 4):D89–D95

    PubMed  Google Scholar 

  3. Cooper S, Dawber R (2001) The history of cryosurgery. J R Soc Med 94:196–201

    PubMed  CAS  Google Scholar 

  4. Felekouras E, Papaconstantinou I, Pikoulis E, Kontos M, Georgopoulos S, Papalois A, Diamantis T, Bramis J, Papalambros E, Bastounis E (2005) Laparoscopic liver resection using radio frequency ablation in a porcine model. Surg Endosc 19:1237–1242

    Article  PubMed  CAS  Google Scholar 

  5. Sojar V, Stanisavljevic D, Hribenrnik M, Glusic M, Kreuh D, Velkrah U, Fius T (2004) Liver surgery training and planning in 3D virtual space. Int Congr Ser 1268:390–394

    Article  Google Scholar 

  6. Fasquel JB, Agnus V, Moreau J, Soler L, Marescaux J (2006) An interactive medical image segmentation system based on the optimal management of regions of interest using topological medical knowledge. Comput Methods Programs Biomed 82:213–230

    Article  Google Scholar 

  7. Meinzer HP, Thorn M, Cardenas CE (2002) Computerized planning of liver surgery - an overview. Comput Graphics 26:569–576

    Article  Google Scholar 

  8. Duncan JS, Ayache N (2000) Medical image analysis: progress over two decades and the challenges ahead. IEEE Trans Pattern Anal Mach Intell 22:85–106

    Article  Google Scholar 

  9. Masutani Y, Uozumi K, Akahane M, Ohtomo K (2006) Liver CT image processing: a short introduction of the technical elements. Eur J Radiol 58:246–251

    Article  PubMed  CAS  Google Scholar 

  10. Van Assen HC, Danilouchkine MG, Frangi AF, Ordas S, Westenberg JJ, Reiber JH, Lelieveldt BP (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10:286–303

    Article  PubMed  Google Scholar 

  11. Gao L, Heath DG, Kuszyk BS, Fishman EK (1996) Automatic liver segmentation technique for three-dimensional visualization of CT data. Radiology 201:359–364

    PubMed  CAS  Google Scholar 

  12. Saitoh T, Tamura Y, Kaneko T (2004) Automatic segmentation of liver region based on extracted blood vessels. Syst Comput Japan 35:633–641

    Article  Google Scholar 

  13. Lamecker H, Lange T, Seebaß M (2004) Segmentation of the liver using a 3D statistical shape model. ZIB Technical Report 04-09

  14. Soler L, Delingette H, Malandain G, Montagnat J, Ayache N, Koehl C, Dourthe O, Malassagne B, Smith M, Mutter D, Marescaux J (2001) Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput Aided Surg 6:131–142

    Article  PubMed  CAS  Google Scholar 

  15. Liu F, Zhao B, Kijewski PK (2005) Liver segmentation for CT images using GVF snake. Med Phys 32:3699–3706

    Article  PubMed  Google Scholar 

  16. Fernandez G, Bischof H, Beichel R (2003) Nonlinear Filters on 3D CT imaging - bilateral filter and mean shift Filter. Proceedings of the 8th Computer Vision Winter Workshop (CVWW’03) 1:21–26

    Google Scholar 

  17. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24:603–619

    Article  Google Scholar 

  18. Bartlett JE, Kotrlik JW, Higgins CC (2001) Organizational research: determining appropriate sample size in survey research. Info Technol Learn Perf J 19:43–50

    Google Scholar 

  19. Kittler J, Illingworth J, Foglein J (1985) Threshold selection based on a simple image statistic. Comput Vision Graph Image Process 30:125–147

    Article  Google Scholar 

  20. Dillencourt MB, Samet H, Tamminen M (1992) A general approach to connected-component labeling for arbitrary image representations. J Assoc Comput Machinery 39:253–280

    Google Scholar 

  21. Soille P (1999) Morphological image analysis: principles and applications. Springer, Berlin Heidelberg, New York

    Google Scholar 

  22. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7:359–369

    Article  PubMed  CAS  Google Scholar 

  23. Cvancarova M, Albregtsen F, Brabrand K, Samset E (2005) Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF. Int Congr Ser 1281:218–223

    Article  Google Scholar 

  24. Knops ZF, Maintz JB, Viergever MA, Pluim JP (2006) Normalized mutual information based registration using k-means clustering and shading correction. Med Image Anal 10(3):432–439

    Article  PubMed  CAS  Google Scholar 

  25. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Statist Soc B 39:1–38

    Google Scholar 

  26. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17:107–145

    Article  Google Scholar 

  27. Ibanez L, Schroeder W, Ng L, Cates J (2005) The ITK software guide second edition. Insight Software Consortium. http://www.itk.org/ItkSoftwareGuide.pdf

  28. ITK-SNAP. University of North Carolina at Chapel Hill, Gerig G, Yushkevich PA. http://www.itksnap.org/

  29. Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, Hirsch BE, Woodburn J (2006) A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph 30:75–87

    Article  PubMed  Google Scholar 

  30. Grau V, Mewes AUJ, Alcañiz M, Kikinis R, Warfield SK (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23:447–458

    Article  PubMed  CAS  Google Scholar 

  31. Zhang J, Ma KK, Meng HE, Chong V (2004) Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine. Proc 7th Int Workshop on Advanced Image Technology (IWAIT’04) 1:207–211

    Google Scholar 

  32. Manousaki AG, Manios AG, Tsompanaki EI, Panayiotides JG, Tsiftsis DD, Kostaki AK, Tosca AD (2006) A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report. Int J Dermatol 45:402–410

    Article  PubMed  Google Scholar 

  33. Hermoye L, Laamari-Azjal I, Cao Z, Annet L, Lerut J, Dawant BM, Van Beers BE (2005) Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods. Radiology 234:171–178

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Caramella and his staff from the Department of Diagnostic and Interventional Radiology, University of Pisa, and also Petter Risholm from the Interventional Center, Rikshospitalet University Hospital, for the datasets and the help provided.

This work was a part of a European project that received research funding from the Research Training Network Marie Curie Action of the Sixth Framework Program of the European Community. As specifically requested by the European Community, the authors state that “this paper reflects only the author’s views and the European Community is not liable for any use that may be made of the information contained therein.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Laurent Massoptier.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Massoptier, L., Casciaro, S. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol 18, 1658–1665 (2008). https://doi.org/10.1007/s00330-008-0924-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-008-0924-y

Keywords

Navigation