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Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

We present a method and a validation study for the nearly automatic segmentation of liver tumors in CTA scans.

Materials and methods

Our method inputs a liver CTA scan and a small number of user-defined seeds. It first classifies the liver voxels into tumor and healthy tissue classes with an SVM classification engine from which a new set of high- quality seeds is generated. Next, an energy function describing the propagation of these seeds is defined over the 3D image. The functional consists of a set of linear equations that are optimized with the conjugate gradients method. The result is a continuous segmentation map that is thresholded to obtain a binary segmentation.

Results

A retrospective study on a validated clinical dataset consisting of 20 tumors from nine patients’ CTA scans from the MICCAI’08 3D Liver Tumors Segmentation Challenge Workshop yielded an average aggregate score of 67, an average symmetric surface distance of 1.76 mm (SD = 0.61 mm) which is better than the 2.0 mm of other methods on the same database, and a comparable volumetric overlap error (33.8 vs. 32.6%). The advantage of our method is that it requires less user interaction compared to other methods.

Conclusion

Our results indicate that our method is accurate, efficient, and robust to wide variety of tumor types and is comparable or superior to other semi-automatic segmentation methods, with much less user interaction.

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References

  1. World Health Organization, WHO handbook for reporting results of cancer treatment (1979)

  2. Eisenhauer E et al (2009) New response evaluation criteria in solid tumours: revised recist guideline (version 1.1). Eur J Cancer 45: 228–247

    Article  PubMed  CAS  Google Scholar 

  3. Goodwin S, Bonilla S, Sacks D, Reed R, Spies J, Landow W, Worthington-Kirsch R (2003) Reporting standards for uterine artery embolization for the treatment of uterine leiomyomata. J Vasc Int Radiol 14: 467S–476S

    Google Scholar 

  4. Tuma R (2006) Sometimes size does not matter: reevaluating recist and tumor response rate endpoints. J Nat Cancer Inst 98: 1272–1274

    Article  PubMed  Google Scholar 

  5. Tran L, Brown M, Goldin J, Yan X, Pais R, McNitt-Gray M, Gjertson D, Rogers S, Aberle D (2004) Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography. Acad Radiol 11: 1355–1360

    Article  PubMed  Google Scholar 

  6. Marten K, Auer F, Schmidt S, Kohl G, Rummeny E, Engelke C (2006) Inadequacy of manual measurements compared to automated CT volumetry in assessment of treatment response of pulmonary metastases using RECIST criteria. Eur Radiol 16: 781–790

    Article  PubMed  Google Scholar 

  7. Popa T, Ibanez L, Levy E, White A, Bruno J, Cleary K (2006) Tumor volume measurement and volume measurement comparison plug-ins for VolView using ITK. SPIE Med Imaging 6141: 395–402

    Google Scholar 

  8. Mortensen E, Barrett W (1998) Interactive segmentation with intelligent scissors. Graph Models Image Process 60: 349–384

    Article  Google Scholar 

  9. Gao L, Heath D, Kuszyk B, Fishman E (1996) Automatic liver segmentation technique for 3D visualization of CT data. Radiology 201: 359–364

    PubMed  CAS  Google Scholar 

  10. Soler L et al (2001) Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comp Aid Surg 6: 131–142

    Article  CAS  Google Scholar 

  11. Chen T, Metaxas D (2005) A hybrid framework for 3D medical image segmentation. Med Image Anal 9: 547–565

    Article  PubMed  Google Scholar 

  12. Bourquain H, Schenk A, Link F, Preim B, Prause G, Peitgen H (2002) Hepavision2a software assistant for preoperative planning in living related liver transplantation and oncologic liver surgery. In: Proceedings of the 16th Conference on Computer Assisted Radiology and Surgery (CARS’02), pp 341–346

  13. Li Y, Hara S, Shimura K (2006) A machine learning approach for locating boundaries of liver tumors in ct images. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR06) , pp 400–403

  14. Jolly M-P, Grady L (2008) 3d general lesion segmentation in ct. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging (ISBI’08), IEEE, pp 796–799

  15. Smeets D, Loeckx D, Stijnen B, De Dobbelaer B, Vandermeulen D, Suetens P (2010) Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification. Med Image Anal 14: 13–20

    Article  PubMed  Google Scholar 

  16. Deng X, Du G (eds) (2008) Proceedings of the 3D Segmentation in the Clinic: a Grand Challenge II—Liver Tumor Segmentation (LTS’08), http://grand-challenge2008.bigr.nl/proceedings/liver/articles.html.

  17. Freiman M, Eliassaf O, Taieb Y, Joskowicz L, Sosna J (2008) A bayesian approach for liver analysis: algorithm and validation study. In: Proceedings of the 11th International Conference of Medical Image Comp. and Computed Aided Intervention (MICCAI’08), vol. 5241 of LNCS, pp 85–92

  18. Taieb Y, Eliassaf O, Freiman M, Joskowicz L, Sosna J (2008) An iterative bayesian approach for liver analysis: tumors validation study. In: Proceedings of the 3D Segmentation in the Clinic: A Grand Challenge II—Liver Tumor Segmentation (LTS’08)

  19. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  20. Lischinski D, Farbman Z, Uyttendaele M, Szeliski R (2006) Interactive local adjustment of tonal values. ACM Trans Graph 25: 646–653

    Article  Google Scholar 

  21. Freiman M, Eliassaf O, Taieb Y, Joskowicz L, Azraq Y, Sosna J (2008) An iterative bayesian approach for nearly automatic liver segmentation: algorithm and validation. Int J Comput Assist Radiol Surgery (IJCARS) 3: 439–446

    Article  Google Scholar 

  22. Li Y, Adelson E, Agarwala A (2008) ScribbleBoost: adding classification to edge-aware interpolation of local image and video adjustments. Comp Graph Forum 27: 1255–1264

    Article  Google Scholar 

  23. Saad Y (2003) Iterative methods for sparse linear systems. SIAM, 2nd edn

  24. Grady L (2008) A lattice-preserving multigrid method for solving the inhomogeneous poisson equations used in image analysis. In: Proceedings of the 10th European Conference on Computer Vision, ECCV’2008 , vol. 5303 of LNCS, Springer, pp 252–264

  25. Ibanez L, Schroeder W, Ng L, Cates J (2005) The ITK Software Guide. Kitware, Inc. ISBN 1-930934-15-7, http://www.itk.org/ItkSoftwareGuide.pdf

  26. http://lts08.bigr.nl/

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Correspondence to Moti Freiman.

Additional information

Moti Freiman and Ofir Cooper are equally contributed.

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Freiman, M., Cooper, O., Lischinski, D. et al. Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation. Int J CARS 6, 247–255 (2011). https://doi.org/10.1007/s11548-010-0497-5

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  • DOI: https://doi.org/10.1007/s11548-010-0497-5

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