Skip to main content

Advertisement

SpringerLink
Log in

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm’s dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.

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

Access this article

Log in via an institution

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Allen DM, Cady FB (1982) Analyzing experimental data by regression. Lifetime Learning Publications, Belmont

    Google Scholar 

  2. Bae E, Yuan J, Tai X-C (2011) Global minimization for continuous multiphase partitioning problems using a dual approach. Int J Comput Vis 92(1):112–129

    Article  Google Scholar 

  3. Ball DL, Fisher RJ, Burmeister BH et al (2013) The complex relationship between lung tumor volume and survival in patients with non-small cell lung cancer treated by definitive radiotherapy: a prospective, observational prognostic factor study of the Trans-Tasman Radiation Oncology Group (TROG 99.05). Radiother Oncol 106:305–311

    Article  PubMed  Google Scholar 

  4. Ballangan C, Wang X, Fulham M, Eberl S, Feng DD (2013) Lung tumor segmentation in PET images using graph cuts. Comput Methods Programs Biomed 109(3):260–268

    Article  PubMed  Google Scholar 

  5. Barbu A, Suehling M, Xun X et al (2012) Automatic detection and segmentation of lymph nodes from CT data. IEEE Trans Med Imaging 31:241–250

    Article  Google Scholar 

  6. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310

    Article  CAS  PubMed  Google Scholar 

  7. Boykov Y, Funka G (2009) Graph cuts and efficient N–D image segmentation. Int J Comput Vis 70:109–131

    Article  Google Scholar 

  8. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26:1124–1137

    Article  PubMed  Google Scholar 

  9. Brennan C, Kajal D et al (2014) Solid malignant retroperitoneal masses—a pictorial review. Insights Imaging 5(1):53–65

    Article  PubMed  Google Scholar 

  10. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  CAS  PubMed  Google Scholar 

  11. Chang H, Zhuang AH, Valentino DJ, Chi WC (2009) Perfomance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage 47:122–135

    Article  PubMed  Google Scholar 

  12. Chen V, Ryan S (2010) Graph cut segmentation technique for MRI brain tumor extraction. In: IPTA, pp 284–287

  13. Chen Q, Quan F, Xu J, Rubin DL (2013) Snake model-based lymphoma segmentation for sequential CT images. Comput Methods Programs Biomed 111(2):366–375

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cremers D, Pock T, Kolev K, Chambolle A (2011) Convex relaxation techniques for segmentation, stereo and multiview reconstruction. In: Blake A, Kohli P, Rother C (eds) Markov random fields for vision and image processing. MIT Press, Boston

  15. DICE coefficient in http://sve.loni.ucla.edu/instructions/metrics/dice/. Accessed 15 July 2014

  16. Feulner J, Zhou SK, Hammon M, Hornegger J, Comaniciu D (2011) Segmentation based features for lymph node detection from 3D Chest CT. LNCS MLMI 7009:91–99

    Google Scholar 

  17. Feulner J, Zhou S, Hammon M et al (2013) Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior. Med Image Anal 17(2):254–270

    Article  PubMed  Google Scholar 

  18. Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378–382

    Article  Google Scholar 

  19. Ford LR, Fulkerson DR (1962) Flows in Networks. Princeton University Press, Princeton

    Google Scholar 

  20. Gao Y, Liao S, Shen D (2012) Prostate segmentation by sparse representation based classification. MICCAI 15:451–458

    PubMed  PubMed Central  Google Scholar 

  21. Goenka AH, Shah SN, Remer EM (2012) Imaging of the retroperitoneum. Radiol Clin N Am 50(2):333–355

    Article  PubMed  Google Scholar 

  22. Gonzalez RC, Woods RE (2008) Digital image processing. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  23. Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37:547–579

    Google Scholar 

  24. Jameson MG, Holloway LC, Vial PJ et al (2010) A review of methods of analysis in contouring studies for radiation oncology. J Med Imaging Radiat Oncol 54:401–410

    Article  PubMed  Google Scholar 

  25. Moradi M, Janoos, F, Fedorov A, Risholm P, Kapur T, Wolfsberger LD, Wells WM (2012) Two solutions for registration of ultrasound to MRI for image-guided prostate interventions. IEEE EMBS, pp 1129–1132

  26. Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113(1):202–209

    Article  PubMed  Google Scholar 

  27. Lermé N, Malgouyres F, Rocchisani JM (2010) Fast and memory efficient segmentation of lung tumors using graph cuts. In: MICCAI, third international workshop on pulmonary image analysis, pp 9–20

  28. Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243–3254

    Article  PubMed  Google Scholar 

  29. Luo Z (2011) Segmentation of liver tumor with local C–V level set. IEEE MACE. doi:10.1109/MACE.2011.5988824

    Google Scholar 

  30. MDCP coefficient in http://my.safaribooksonline.com/book/biotechnology/9781627054300/methods-for-evaluation-of-the-results/distance_measures. Accesed 15 July 2014

  31. Moschidis E, Graham J (2010) Interactive differential segmentation of the prostate using graph-cuts with a feature detector-based boundary term. MIUA, pp 191–195

  32. Osher S, Sethian J (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1):12–49

    Article  Google Scholar 

  33. Perez-Carrasco J, Suárez-Mejias C, Serrano C, López-Guerra J, Acha B (2013) Segmentation of retroperitoneal tumors using fast continuous max-flow algorithm. MEDICON 41:360–363

    Google Scholar 

  34. Plajer I, Nguyen-Pham T-K, Richter D (2009) Tumour segmentation by active contours in 3D CT wavelet enhanced image data. EUSIPCO

  35. Punithakumar K, Yuan J et al (2012) A convex max-flow approach to distribution-based figure-ground separation. SIAM J Imaging Sci 5(4):1333–1354

    Article  Google Scholar 

  36. Qiu W, Yuan J, Kishimoto J, Ukwatta E, Fenster A (2013) Lateral ventricle segmentation of 3D pre-term neonates US using convex optimization. MICCAI 16:559–566

    PubMed  Google Scholar 

  37. Qiu W, Yuan J, Ukwatta E, Yue S, Rajchl M, Fenster A (2014) Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images. IEEE Trans Med Imaging 33(4):947–960

    Article  PubMed  Google Scholar 

  38. Rajchl M, Yuan White J, Ukwatta E, Stirrat J, Nambakhsh C, Peters T (2014) Interactive hierarchical max-flow segmentation of scar tissue from late-enhancement cardiac MR images. IEEE Trans Med Imaging 33(1):159–172

    Article  PubMed  Google Scholar 

  39. Rajiah P, Sinha R, Cuevas C, Dubinsky TJ, Bush WH, Kolokythas O (2011) Imaging of uncommon retroperitoneal masses. RadioGraphics 31:949–976. doi:10.1148/rg.314095132

    Article  PubMed  Google Scholar 

  40. Rockafeller R, Wets RJB (1998) Variational analysis. Grundlehren der mathematischen Wissenschaften 317 Springer Verlag

  41. Rosenfeld A, Pfaltz JL (1968) Distance functions on digital pictures. Pattern Recogn 1(1):33–61

    Article  Google Scholar 

  42. Song Q, Chen M, Bai J et al (2011) Surface region context in optimal multi-object graph based segmentation: robust delineation of pulmonary tumors. IPMI, pp 61–72

  43. Strang G (1983) Maximal flow through a domain. Math Program 26(2):123–143

    Article  Google Scholar 

  44. Sun T, Wang J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X (2013) Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. Comput Methods Programs Biomed 111(2):519–524

    Article  PubMed  Google Scholar 

  45. Ukwatta E, Yuan J, Rajchl M, Qiu W, Tessier D, Fenster A (2013) 3-D carotid multi-region MRI segmentation by globally optimal evolution of coupled surfaces. IEEE Trans Med Imaging 32(4):770–785

    Article  PubMed  Google Scholar 

  46. Ukwatta E, Yuan J, Qiu W, Rajchl M, Chiu B, Shavakh S, Xu J, Fenster A (2013) Joint segmentation of 3D femoral lumen and outer wall surfaces from MR images. MICCAI 8149:534–541

    Google Scholar 

  47. Vincent L (1998) Minimal path algorithms for the robust detection of linear features in gray images. ISSM, pp 331–338

  48. Wook-Jin C, Tae-Sun C (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Programs Biomed 113(1):37–54

    Article  Google Scholar 

  49. Yin X, Ng WH, Yang Q, Pitman A, Ramamohanarao K, Abbott D (2012) Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging. Med Biol Eng Comput 50(1):91–101

    Article  CAS  PubMed  Google Scholar 

  50. Yuan J, Bae E et al (2010) A study on continuous max-flow and min-cut approaches. In: CVPR, pp 2217–2224

  51. Yuan J, Bae E, Tai XC, Boykov Y (2010) A continuous max-flow approach to Potts model. LNCS ECCV 6316:379–392

    Google Scholar 

  52. Yuan J, Ukwatta E, Tai X C, Fenster A, Schnoerr C (2012) A fast global optimization-based approach to evolving contours with generic shape prior. UCLA Tech. Report CAM 12-38

  53. Yuan J, Qiu W, Rajchl M, Ukwatta E, Xue-Cheng T, Fenster A (2013) Efficient 3D endfiring TRUS prostate segmentation with globally optimized rotational symmetry. In: IEEE CVPR, pp 2211–2218

  54. Zhang J, Wang Y, Shi X (2009) An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node’s shape assessment. Comput Med Imaging Graph 33:602–607

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This research was co-financed by TEC2010-21619-C04-02 (Government of Spain), P11-TIC-7727 (Regional Government of Andalusia, Spain), PT13/0006/0036 RETIC, FEDER Funds and Department of Health (Regional Government of Andalusia). We would like to thank Jose Manuel Conde and María José Ortíz for their clinical contribution to the development of this algorithm.

Author information

Authors and Affiliations

  1. Technological Innovation Group, Virgen del Rocío University Hospital, Seville, Spain

    Cristina Suárez-Mejías & Carlos Parra-Calderón

  2. Signal Theory and Communications Department, University of Seville, Seville, Spain

    Cristina Suárez-Mejías, Jose Antonio Pérez-Carrasco, Carmen Serrano & Begoña Acha

  3. Oncology Unit, Virgen del Rocío University Hospital, Seville, Spain

    Jose Luis López-Guerra

  4. Surgery Unit, Virgen del Rocío University Hospital, Seville, Spain

    Tomás Gómez-Cía

Authors
  1. Cristina Suárez-Mejías
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jose Antonio Pérez-Carrasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carmen Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jose Luis López-Guerra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carlos Parra-Calderón
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tomás Gómez-Cía
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Begoña Acha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Suárez-Mejías.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suárez-Mejías, C., Pérez-Carrasco, J.A., Serrano, C. et al. Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning. Med Biol Eng Comput 55, 1–15 (2017). https://doi.org/10.1007/s11517-016-1505-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-016-1505-x

Keywords

Search

Navigation