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Abdomen and Thoracic Imaging pp 69–97Cite as

Renal Cortex Segmentation on Computed Tomography

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Abstract

The current procedure of renal cortex segmentation is subjective and tedious. This chapter introduces an automated framework for renal cortex segmentation on contrast-enhanced abdominal CT images. The framework consists of four parts: first, an active appearance model (AAM) is built using a set of training images; second, the AAM is refined by live wire (LW) method to initialize the shape and location of the kidney; third, an iterative graph cut-oriented active appearance model (IGC-OAAM) method is applied to segment the kidney; Finally, the identified kidney contour is used as shape constraints for renal cortex segmentation which is also based on IGC-OAAM. The chapter also discusses several other state-of-art techniques for segmentation and modeling of the kidneys.

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References

  1. American Cancer Society (2010) http://wwwcancerorg/Cancer/KidneyCancer/DetailedGuide/kidney-cancer-adult-key-statistics

    Google Scholar 

  2. Rathmell WK, Martz CA, Rini BI (2007) Renal cell carcinoma. Curr Opin Oncol 19(3):234–240

    Article  PubMed  Google Scholar 

  3. Beland MD, Walle NL, Machan JT, Cronan JJ (2010) Renal cortical thickness measured at ultrasound: is it better than renal length as an indicator of renal function in chronic kidney disease? Am J Roentgenol 195(2):W146–W149

    Article  Google Scholar 

  4. Muto NS, Kamishima T, Harris AA, Kato F, Onodera Y, Terae S, Shirato H (2011) Renal cortical volume measured using automatic contouring software for computed tomography and its relationship with BMI, age and renal function. Eur J Radiol 78(1):151–156

    Article  PubMed  Google Scholar 

  5. Artunc F, Yildiz S, Rossi C, Boss A, Dittmann H, Schlemmer H, Risler T, Heyne N (2010) Simultaneous evaluation of renal morphology and function in live kidney donors using dynamic magnetic resonance imaging. Nephrol Dial Transplant 25(6):1986–1991

    Article  PubMed  CAS  Google Scholar 

  6. Stevens LA, Coresh J, Greene T, Levey AS (2006) Assessing kidney function—measured and estimated glomerular filtration rate. N Engl J Med 354(23):2473–2483

    Article  PubMed  CAS  Google Scholar 

  7. Li X, Chen X, Yao J, Zhang X, Yang F, Tian J (2012) Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search. IEEE Trans Med Imaging 31(10):1849–1860

    Article  PubMed  Google Scholar 

  8. American Cancer Society (2013) How is kidney cancer diagnosed? http://wwwcancerorg/cancer/kidneycancer/detailedguide/kidney-cancer-adult-diagnosis

    Google Scholar 

  9. Medicine JH. Computed tomography (CT or CAT) scan of the kidney. http://wwwhopkinsmedicineorg/healthlibrary/test_procedures/urology/computed_tomography_ct_or_cat_scan_of_the_kidney_92,P07703/

    Google Scholar 

  10. Nikken J, Krestin G (2007) MRI of the kidney—state of the art. Eur Radiol 17(11):2780–2793

    Article  PubMed  CAS  Google Scholar 

  11. Cohen D, Brown JJ (2008) MR imaging of indeterminate renal lesions. Appl Radiol 37(11)

    Google Scholar 

  12. Rusko L, Bekes G, Nemeth G, Fidrich M (2007) Fully automatic liver segmentation for contrast-enhanced CT images. In: MICCAI workshop 3D segmentation in the clinic: a grand challenge, vol 2(7)

    Google Scholar 

  13. Fujimoto H, Gu L, Kaneko T (2002) Recognition of abdominal organs using 3D mathematical morphology. Syst Comput Japan 33(8):75–83

    Article  Google Scholar 

  14. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  16. Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175

    Article  Google Scholar 

  17. Falcão AX, Udupa JK, Samarasekera S, Sharma S, Hirsch BE (1998) Lotufo RdA: user-steered image segmentation paradigms: live wire and live lane. Graph Mod Image Process 60(4):233–260

    Article  Google Scholar 

  18. Grau V, Mewes A, Alcaniz M, Kikinis R, Warfield SK (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23(4):447–458

    Article  PubMed  CAS  Google Scholar 

  19. Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Mod Image Process 58(3):246–261

    Article  Google Scholar 

  20. Saha PK, Udupa JK (2001) Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation. Comput Vis Image Underst 82(1):42–56

    Article  Google Scholar 

  21. 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(9):1124–1137

    Article  PubMed  Google Scholar 

  22. Kolmogorov V, Zabin R (2004) What energy functions can be minimized via graph cuts? IEEE Trans Pattern Anal Mach Intell 26(2):147–159

    Article  PubMed  Google Scholar 

  23. Besbes A, Komodakis N, Langs G, Paragios N (2009) Shape priors and discrete MRFs for knowledge-based segmentation. In: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009), IEEE, pp 1295–1302

    Google Scholar 

  24. Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure J-G, Thiran J-P (2004) Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imaging 23(10):1301–1314

    Article  PubMed  Google Scholar 

  25. Bazin P-L, Pham DL (2008) Homeomorphic brain image segmentation with topological and statistical atlases. Med Image Anal 12(5):616

    Article  PubMed  Google Scholar 

  26. Frangi AF, Rueckert D, Schnabel JA, Niessen WJ (2002) Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging 21(9):1151–1166

    Article  PubMed  Google Scholar 

  27. Mitchell SC, Lelieveldt BPF, van der Geest RJ, Bosch HG, Reiver J, Sonka M (2001) Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 20(5):415–423

    Article  PubMed  CAS  Google Scholar 

  28. Bankman I (2000) Handbook of medical imaging: processing and analysis management. Academic, New York

    Google Scholar 

  29. Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solorzano C (2009) Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging 28(8):1266–1277

    Article  PubMed  Google Scholar 

  30. Christensen GE, Rabbitt RD, Miller MI (1994) 3D brain mapping using a deformable neuroanatomy. Phys Med Biol 39(3):609

    Article  PubMed  CAS  Google Scholar 

  31. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  32. Stegmann MB, Ersboll BK, Larsen R (2003) FAME-a flexible appearance modeling environment. IEEE Trans Med Imaging 22(10):1319–1331

    Article  PubMed  Google Scholar 

  33. Liu J, Udupa JK (2009) Oriented active shape models. IEEE Trans Med Imaging 28(4):571–584

    Article  PubMed  CAS  Google Scholar 

  34. Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK (1998) Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 7(12):1684–1699

    Article  PubMed  CAS  Google Scholar 

  35. Yang J, Duncan JS (2004) 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Med Image Anal 8(3):285

    Article  PubMed  Google Scholar 

  36. Freedman D, Zhang T (2005) Interactive graph cut based segmentation with shape priors. In: IEEE computer society conference on computer vision and pattern recognition, 2005 (CVPR 2005), IEEE, pp 755–762

    Google Scholar 

  37. Ayvaci A, Freedman D (2007) Joint segmentation-registration of organs using geometric models. In: 29th annual international conference of the IEEE engineering in medicine and biology society, 2007 (EMBS 2007), IEEE, pp 5251–5254

    Google Scholar 

  38. Malcolm J, Rathi Y, Tannenbaum A (2007) Graph cut segmentation with nonlinear shape priors. In: IEEE international conference on image processing, 2007 (ICIP 2007), IEEE, pp IV-365–IV-368

    Google Scholar 

  39. Vu N, Manjunath B (2008) Shape prior segmentation of multiple objects with graph cuts. In: IEEE conference on computer vision and pattern recognition, 2008 (CVPR 2008), IEEE, pp 1–8

    Google Scholar 

  40. Haas B, Coradi T, Scholz M, Kunz P, Huber M, Oppitz U, André L, Lengkeek V, Huyskens D, van Esch A (2008) Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies. Phys Med Biol 53(6):1751

    Article  PubMed  CAS  Google Scholar 

  41. 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

    Article  PubMed  Google Scholar 

  42. Seifert S, Barbu A, Zhou SK, Liu D, Feulner J, Huber M, Suehling M, Cavallaro A, Comaniciu D (2009) Hierarchical parsing and semantic navigation of full body CT data. In: SPIE Medical imaging, International Society for Optics and Photonics, pp 725902–725908

    Google Scholar 

  43. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  44. Rusinek H, Boykov Y, Kaur M, Wong S, Bokacheva L, Sajous JB, Huang AJ, Heller S, Lee VS (2007) Performance of an automated segmentation algorithm for 3D MR renography. Magn Reson Med 57(6):1159–1167

    Article  PubMed  Google Scholar 

  45. Boykov Y, Lee VS, Rusinek H, Bansal R (2001) Segmentation of dynamic ND data sets via graph cuts using Markov models. In: Medical image computing and computer-assisted intervention—MICCAI 2001, Springer, pp 1058–1066

    Google Scholar 

  46. Sun Y, Jolly M-P, Moura J (2004) Integrated registration of dynamic renal perfusion MR images. In: International conference on image processing, 2004 (ICIP’04 2004), IEEE, pp 1923–1926

    Google Scholar 

  47. Zöllner FG, Sance R, Rogelj P, Ledesma-Carbayo MJ, Rørvik J, Santos A, Lundervold A (2009) Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Comput Med Imaging Graph 33(3):171–181

    Article  PubMed  Google Scholar 

  48. Song T, Lee VS, Rusinek H, Bokacheva L, Laine A (2008) Segmentation of 4D MR renography images using temporal dynamics in a level set framework. In: 5th IEEE international symposium on biomedical imaging: from nano to macro, 2008 (ISBI 2008), IEEE, pp 37–40

    Google Scholar 

  49. Freiman M, Kronman A, Esses S, Joskowicz L, Sosna J (2010) Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation. In: Medical image computing and computer-assisted intervention—MICCAI 2010, Springer, pp 73–80

    Google Scholar 

  50. Tsagaan B, Shimizu A, Kobatake H, Miyakawa K (2002) An automated segmentation method of kidney using statistical information. In: Medical image computing and computer-assisted intervention—MICCAI 2002, Springer, pp 556–563

    Google Scholar 

  51. Touhami W, Boukerroui D, Cocquerez J-P (2005) Fully automatic kidneys detection in 2D ct images: a statistical approach. In: Medical image computing and computer-assisted intervention—MICCAI 2005, Springer, pp 262–269

    Google Scholar 

  52. Gloger O, Tonnies KD, Liebscher V, Kugelmann B, Laqua R, Volzke H (2012) Prior shape level set segmentation on multistep generated probability maps of MR datasets for fully automatic kidney parenchyma volumetry. IEEE Trans Med Imaging 31(2):312–325

    Article  PubMed  Google Scholar 

  53. Lin D-T, Lei C-C, Hung S-W (2006) Computer-aided kidney segmentation on abdominal CT images. IEEE Trans Inf Technol Biomed 10(1):59–65

    Article  PubMed  Google Scholar 

  54. Xie J, Jiang Y, Tsui H-T (2005) Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging 24(1):45–57

    Article  PubMed  Google Scholar 

  55. Li X, Chen X, Yao J, Zhang X, Tian J (2011) Renal cortex segmentation using optimal surface search with novel graph construction. In: Medical image computing and computer-assisted intervention—MICCAI 2011, Springer, pp 387–394

    Google Scholar 

  56. Spiegel M, Hahn DA, Daum V, Wasza J, Hornegger J (2009) Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration. Comput Med Imaging Graph 33(1):29–39

    Article  PubMed  Google Scholar 

  57. Dryden I, Mardia K (1998) Statistical analysis of shape. Wiley, Chichester

    Google Scholar 

  58. Mitchell SC, Bosch JG, Lelieveldt BPF, van der Geest RJ, Reiber JHC, Sonka M (2002) 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Trans Med Imaging 21(9):1167–1178

    Article  PubMed  Google Scholar 

  59. 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(2):75–87

    Article  PubMed  Google Scholar 

  60. Emrich T, Graf F, Kriegel H-P, Schubert M, Thoma M, Cavallaro A (2010) CT slice localization via instance-based regression. In: Proceedings of the SPIE medical imaging, p 762320

    Google Scholar 

  61. Chen X, Udupa JK, Bagci U, Zhuge Y, Yao J (2012) Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans Image Process 21(4):2035–2046

    Article  PubMed  Google Scholar 

  62. 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

    Article  Google Scholar 

  63. Anderson R, Setubal JC (1995) A parallel implementation of the push-relabel algorithm for the maximum flow problem. J Parallel Distrib Comput 29(1):17–26

    Article  Google Scholar 

  64. Liu J, Sun J (2010) Parallel graph-cuts by adaptive bottom-up merging. In: IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp 2181–2188

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Soochow University, Suzhou, China

    Xinjian Chen, Dehui Xiang, Wei Ju & Heming Zhao

  2. Department of Radiology and Imaging Sciences, National Institute of Health, Bethesda, MD, 20892, USA

    Jianhua Yao

Authors
  1. Xinjian Chen
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  2. Dehui Xiang
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  3. Wei Ju
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  4. Heming Zhao
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  5. Jianhua Yao
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Corresponding author

Correspondence to Xinjian Chen .

Editor information

Editors and Affiliations

  1. Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA

    Ayman S. El-Baz

  2. University of Cagliari, Cagliari, Italy

    Luca Saba

  3. Biomedical Technologies, Inc Idaho State University (Affiliated), Roseville, California, USA

    Jasjit Suri

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Chen, X., Xiang, D., Ju, W., Zhao, H., Yao, J. (2014). Renal Cortex Segmentation on Computed Tomography. In: El-Baz, A., Saba, L., Suri, J. (eds) Abdomen and Thoracic Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8498-1_3

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  • DOI: https://doi.org/10.1007/978-1-4614-8498-1_3

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