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Journal of Digital Imaging

, Volume 31, Issue 6, pp 799–850 | Cite as

3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

  • L. E. CarvalhoEmail author
  • A. C. Sobieranski
  • A. von Wangenheim
Article

Abstract

This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006—March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.

Keywords

3D segmentation Computerized tomographic imaging Kitchenham’s systematic review Segmentation methods categorization 

References

  1. 1.
    Gonzalez RC, Woods RE: Digital image processing, 2nd edition. Upper Saddle River: Prentice-Hall, Inc., 2006Google Scholar
  2. 2.
    Ilea DE, Whelan PF: Image segmentation based on the integration of colour-texture descriptors- a review. Pattern Recogn 44(10-11):2479–2501, 2011. Semi-Supervised Learning for Visual Content Analysis and UnderstandingGoogle Scholar
  3. 3.
    Ma Z, Tavares J, Jorge RN: A review on the current segmentation algorithms for medical images, 2009, pp 135–140, 01Google Scholar
  4. 4.
    Ma Z, Tavares JMRS, Jorge RM, Mascarenhas TR: Natal a review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Eng 13(2):235–46, 2010Google Scholar
  5. 5.
    Jodas DS, Pereira AS, Tavares JMRS: A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst Appl 46:1–14, 2016Google Scholar
  6. 6.
    Kitchenham B: Procedures for Performing Systematic Reviews. Technical report Joint Technical Report TR/SE-0401, 2004Google Scholar
  7. 7.
    National Health, Medical Research Council (Australia), and Nhmrc Staff. How to Review the Evidence: Systematic Identification and Review of the Scientific Literature. Handbook series on preparing clinical practice guidelines. National Health and Medical Research Council, 2000Google Scholar
  8. 8.
    National Health, Medical Research Council (Australia), and Nhmrc Staff. How to Use the Evidence: Assessment and Application of Scientific Evidence. Handbook series on preparing clinical practice guidelines. National Health and Medical Research Council, 2000Google Scholar
  9. 9.
    Cochrane Collaboration. Cochrane Reviewers Handbook. Version 4.2.1. National Health and Medical Research Council, 2003Google Scholar
  10. 10.
    University of York. NHS Centre for Reviews and Dissemination. Undertaking systematic reviews of research on effectiveness: CRD’s guidance for those carrying out or commissioning reviews. CRD report. NHS Centre for Reviews and Dissemination, University of York, 2001Google Scholar
  11. 11.
    Katz YH: Automatic pattern recognition of meteorological satellite cloud photography, 1964Google Scholar
  12. 12.
    Joan SW: A survey of threshold selection techniques. Comput Graph Image Process 7(2):259–265, 1978Google Scholar
  13. 13.
    Li Z, Lee K, Niemeijer M, Mullins RF, Sonka M, Abramoff MD: Automated segmentation of the choroid from clinical sd-oct. Investig Ophthalmol Vis Sci 53(12):7510–7519, 2012Google Scholar
  14. 14.
    Javaid M, Javid M, Rehman MZU, Shah SIA: A novel approach to {CAD} system for the detection of lung nodules in {CT} images. Comput Methods Programs Biomed 135:125–139, 2016PubMedGoogle Scholar
  15. 15.
    Armato SG, McLennan Gx, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, et al: The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Med Phys 38(2):915–931, 2011PubMedPubMedCentralGoogle Scholar
  16. 16.
    Chang KY, Wu YH, Lin WL, Chen SJ, Chen LS: Vessel segmentation based on bone-to-bone elimination in brain ct angiography. In 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2016, pp 1–2Google Scholar
  17. 17.
    Migliori S, Chiastra C, Bologna M, Montin E, Dubini G, Aurigemma C, Fedele R, Burzotta F, Mainardi L, Migliavacca F: A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images. Med Eng Phys 47:105–116, 2017PubMedGoogle Scholar
  18. 18.
    Farzaneh N, Habbo-Gavin S, Soroushmehr SMR, Patel H, Fessell DP, Ward KR, Najarian K: Atlas based 3d liver segmentation using adaptive thresholding and superpixel approaches. In: 2017 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP), 2017, pp 1093–1097Google Scholar
  19. 19.
    Boykov Y, Veksler O, Zabih R: Markov random fields with efficient approximations. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ’98. IEEE Computer Society, Washington, 1998, p 648Google Scholar
  20. 20.
    Yi F, Moon I: Image segmentation: a survey of graph-cut methods. In: 2012 International Conference on Systems and Informatics (ICSAI), 2012, pp 1936–1941Google Scholar
  21. 21.
    Wieclawek W, Pietka E: Live-wire-based 3d segmentation method. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp 5645–5648Google Scholar
  22. 22.
    Liu J, Udupa JK, Saha PK, Odhner D, Hirsch BE, Siegler S, Simon S, Winkelstein BA: Rigid model-based 3d segmentation of the bones of joints in mr and ct images for motion analysis. Med Phys 35(8):3637–3649, 2008PubMedPubMedCentralGoogle Scholar
  23. 23.
    Aslan MS, Ali A, Arnold B, Fahmi R, Farag AA, Xiang P: Segmentation of trabecular bones from vertebral bodies in volumetric ct spine images. In: 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp 3385–3388Google Scholar
  24. 24.
    Aslan MS, Ali A, Rara H, Arnold B, Fahmi R, Farag AA, Xiang P: A novel, fast, and complete 3d segmentation of vertebral bones. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, pp 654–657Google Scholar
  25. 25.
    Lu K, Higgins WE: Segmentation of the central-chest lymph nodes in 3d {MDCT} images. Comput Biol Med 41(9):780–789, 2011PubMedPubMedCentralGoogle Scholar
  26. 26.
    Liu X, Tuncali K, Wells WM, Morrison PR, Zientara GP: Fully automatic 3d segmentation of iceball for image-guided cryoablation. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp 2327–2330Google Scholar
  27. 27.
    Boykov Y, Kolmogorov V: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137, 2004PubMedGoogle Scholar
  28. 28.
    Beichel R, Bornik A, Bauer C, Sorantin E: Liver segmentation in contrast enhanced ct data using graph cuts and interactive 3d segmentation refinement methods. Med Phys 39(3):1361–1373, 2012PubMedPubMedCentralGoogle Scholar
  29. 29.
    Beichel RR, Wang Y: Computer-aided lymph node segmentation in volumetric ct data. Med Phys 39(9):5419–5428, 2012PubMedPubMedCentralGoogle Scholar
  30. 30.
    Chen X, Niemeijer M, Zhang L, Lee K, Abramoff MD, Sonka M: 3D segmentation of fluid-associated abnormalities in retinal oct Probability constrained graph-search-graph-cut. IEEE Trans Med Imaging 31(8):1521–1531, 2012PubMedPubMedCentralGoogle Scholar
  31. 31.
    Pazokifard B, Sowmya A: 3-d segmentation of human sternum in lung mdct images. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, pp 3351–3354Google Scholar
  32. 32.
    Grosgeorge D, Petitjean C, Dubray B, Su R: Esophagus segmentation from 3d ct data using skeleton prior-based graph cut. Computational and mathematical methods in medicine, 2013Google Scholar
  33. 33.
    El-Zehiry N, Jolly MP, Sofka M: A splice-guided data driven interactive editing. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, 2013, pp 1098–1101Google Scholar
  34. 34.
    Antony BJ, Miri MS, Abràmoff MD, Kwon YH, Garvin MK, Howe R: Automated 3d segmentation of multiple surfaces with a shared hole: Segmentation of the neural canal opening in sd-oct volumes. In: (Golland P, Hata N, Barillot C, Hornegger J, Eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, Cham, 2014, pp 739–746Google Scholar
  35. 35.
    Jodoin PM, Pinheiro F, Oudot A, Lalande A: Left-ventricle segmentation of spect images of rats. IEEE Trans Biomed Eng 62(9):2260–2268, 2015PubMedGoogle Scholar
  36. 36.
    Vasquez D, Scharcanski J, Wong A: Automatic framework for extraction and characterization of wetting front propagation using tomographic image sequences of water infiltrated soils. PLOS ONE 01(1):1–12, 2015Google Scholar
  37. 37.
    Kitrungrotsakul T, Chen Y-W, Han X-H, Lin L: Supervoxels based graph cut for medical organ segmentation. IFAC-PapersOnLine 48(20):70–75, 2015. 9th {IFAC} Symposium on Biological and Medical Systems {BMS} 2015Berlin, Germany, 31 August-2 September 2015Google Scholar
  38. 38.
    Gangsei LE, Kongsro J: Automatic segmentation of computed tomography (ct) images of domestic pig skeleton using a 3d expansion of dijkstra’s algorithm. Comput Electron Agric 121:191–194, 2016Google Scholar
  39. 39.
    Cha JW, Farhangi MM, Dunlap N, Amini A: Volumetric analysis of respiratory gated whole lung and liver ct data with motion-constrained graph cuts segmentation. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp 3405–3408Google Scholar
  40. 40.
    Osher S, Sethian JA: Fronts propagating with curvature dependent speed: algorithms based on hamilton-jacobi formulations. J Comput Phys 79(1):12–49, 1988Google Scholar
  41. 41.
    Burger M, Osher SJ: A survey on level set methods for inverse problems and optimal design, 2004Google Scholar
  42. 42.
    Liu J-W, Feng H-Q, Zhou Y-Y, LI C-F: A novel automatic extraction method of lung texture tree from {HRCT} images. Acta Autom Sin 35(4):345–349, 2009Google Scholar
  43. 43.
    Chen D, Fahmi R, Farag AA, Falk RL, Dryden, GW: Accurate and fast 3d colon segmentation in ct colonography. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp 490–493Google Scholar
  44. 44.
    Chen D, Farag AA, Falk RL, Dryden, GW: A variational framework for 3d colonic polyp visualization in virtual colonoscopy. In: 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp 2617–2620Google Scholar
  45. 45.
    Hadjiiski L, Mukherji SK, Ibrahim M, Sahiner B, Gujar SK, Moyer J, Chan H-P: Head and neck cancers on ct: preliminary study of treatment response assessment based on computerized volume analysis. Amer J Roentgenol 194(4):1083–1089, 2010Google Scholar
  46. 46.
    Hadjiiski L, Mukherji SK, Gujar SK, Sahiner B, Ibrahim M, Street E, Moyer J, Worden FP, Chan H-P: Treatment response assessment of head and neck cancers on ct using computerized volume analysis. Amer J Neuroradiol 31(9):1744–1751, 2010Google Scholar
  47. 47.
    Hadjiiski L, Weizer AZ, Alva A, Caoili EM, Cohan RH, Cha K, Chan H-P: Treatment response assessment for bladder cancer on ct based on computerized volume analysis, world health organization criteria, and recist. Amer J Roentgenol 205(2):348–352, 2015Google Scholar
  48. 48.
    Hutter J, Hofmann HG, Grimm R, Greiser A, Saake M, Hornegger J, Dörfler A, Schmitt P: Prior-based automatic segmentation of the carotid artery lumen in tof mra (pascal) Berlin: Springer, 2012, pp 511–518Google Scholar
  49. 49.
    Qi Y, Dong K, Yin L, Li M: 3d segmentation of the lung based on the neighbor information and curvature. In: Proceedings of the 2013 Seventh International Conference on Image and Graphics, ICIG ’13. IEEE Computer Society, Washington, 2013, pp 139–143Google Scholar
  50. 50.
    Hemmati H, Kamli-Asl A, Talebpour A, Shirani S: Semi-automatic 3d segmentation of carotid lumen in contrast-enhanced computed tomography angiography images. Phys Med 31(8):1098–1104, 2015PubMedGoogle Scholar
  51. 51.
    Badura P, Wieclawek W: Calibrating level set approach by granular computing in computed tomography abdominal organs segmentation. Appl Soft Comput 49(C):887–900, 2016Google Scholar
  52. 52.
    IRCAD France. 3dircadb, 3d image reconstruction for comparison of algorithm database, Available from: http://www.ircad.fr/research/3d-ircadb-01/. Accessed 19.04.18
  53. 53.
    Suetens P, Verbeeck R, Delaere D, Nuyts J, Bijnens (Auth.) B, Stefanelli M, Hasman A, Fieschi M, Talmon J: AIME 91: Proceedings of the Third Conference on Artificial Intelligence in Medicine, Maastricht, June 24-27, Lecture Notes in Medical Informatics, vol 44, 1st edition. Berlin: Springer, 1991Google Scholar
  54. 54.
    Geman S, Geman D: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell PAMI-6(6):721–741, 1984Google Scholar
  55. 55.
    Huang, Rui, Pavlovic V, Metaxas DN: A tightly coupled region-shape framework for 3d medical image segmentation. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006, pp 426–429Google Scholar
  56. 56.
    Juslin A, Tohka, J: Unsupervised segmentation of cardiac pet transmission images for automatic heart volume extraction. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp 1077–1080Google Scholar
  57. 57.
    Müller O, Donner S, Klinder T, Dragon R, Bartsch I, Witte F, Krüger A, Heisterkamp A, Rosenhahn B: Model based 3d segmentation and oct image undistortion of percutaneous implants. In: Proceedings of the 14th International Conference on Medical Image Computing and Computer-assisted Intervention - Volume Part III, MICCAI’11. Springer, Berlin, 2011, pp 454–462Google Scholar
  58. 58.
    Bhole C, Morsillo N, Pal C: 3d segmentation in ct imagery with conditional random fields and histograms of oriented gradients. In: Proceedings of the Second International Conference on Machine Learning in Medical Imaging, MLMI’11. Springer, Berlin, 2011, pp 326–334Google Scholar
  59. 59.
    Mesejo P, Ibánez Ó, Cordón Ó, Cagnoni S: A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl Soft Comput 44:1–29, 2016Google Scholar
  60. 60.
    Terzopoulos D, Fleischer K: Deformable models. Vis Comput 4(6):306–331, 1988. cited By 392Google Scholar
  61. 61.
    Saragaglia A, Fetita C, Prêteux F: Assessment of airway remodeling in asthma: Volumetric versus surface quantification approaches. In: Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II, MICCAI’06. Springer, Berlin, 2006, pp 413–420Google Scholar
  62. 62.
    Zhang H, Yang L, Foran DJ, Nosher JL, Yim PJ: 3d segmentation of the liver using free-form deformation based on boosting and deformation gradients. In: Proceedings of the Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro, ISBI’09. IEEE Press, Piscataway, 2009, pp 494–497Google Scholar
  63. 63.
    Ding, F, Yang W, Leow WK, Venkatesh SK: 3d segmentation of soft organs by flipping-free mesh deformation. In: 2009 Workshop on Applications of Computer Vision (WACV), 2009, pp 1–7Google Scholar
  64. 64.
    Cascio D, Magro R, Fauci F, Iacomi M, Raso G: Automatic detection of lung nodules in ct datasets based on stable 3d mass-spring models. Comput Biol Med 42(11):1098–1109, 2012PubMedGoogle Scholar
  65. 65.
    Delibasis KK, Christodoulidis A, Maglogiannis I: An intelligent tool for anatomical object segmentation using deformable surfaces. In: Proceedings of the 7th Hellenic Conference on Artificial Intelligence: Theories and Applications, SETN’12. Springer, Berlin, 2012, pp 206–213Google Scholar
  66. 66.
    Shi C, Guo C, Cheng Y, Wang J: Greedy algorithm based deformable simplex meshes using gradient vector flow as external energy. In: 2014 7th International Conference on Biomedical Engineering and Informatics, 2014, pp 199–204Google Scholar
  67. 67.
    Lu D, Wu Y, Harris G, Cai W: Iterative mesh transformation for 3d segmentation of livers with cancers in ct images. Comput Med Imaging Graph 43:1–14, 2015PubMedPubMedCentralGoogle Scholar
  68. 68.
    Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. Int J Comput Vis 1(4):321–331, 1988Google Scholar
  69. 69.
    Jiang H, Cheng Q: Automatic 3d segmentation of ct images based on active contour models. In: 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2009. CAD/graphics ’09, 2009, pp 540–543Google Scholar
  70. 70.
    Barbosa D, Dietenbeck T, Schaerer J, D’hooge J, Friboulet D, Bernard O: B-spline explicit surfaces: active an efficient framework for real-time 3-d region-based segmentation. IEEE Trans Image Process 21(1):241–251, 2012PubMedGoogle Scholar
  71. 71.
    Qi D, Angelini ED, Laine AF: Real-time segmentation by active geometric functions. Comput Methods Prog Biomed 98(3):223–230, 2010Google Scholar
  72. 72.
    Urschler M, Bornik A, Scheurer E, Yen K, Bischof H, Schmalstieg D: Forensic-case analysis: from 3d imaging to interactive visualization. IEEE Comput Graph Appl 32(4):79–87, 2012PubMedGoogle Scholar
  73. 73.
    Mezlini H, Youssef R, Bouhadoun H, Budyn E, Denis Laredo J, Ghalila SS, Chappard C: High resolution volume quantification of the knee joint space based on a semi-automatic segmentation of computed tomography images. In: 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), 2015, pp 157–161Google Scholar
  74. 74.
    Akkus Z, Sedlar J, Coufalova L, Korfiatis P, Kline TL, Warner JD, Agrawal J, Erickson BJ: Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging. Cancer Imaging 15(1):12, 2015PubMedPubMedCentralGoogle Scholar
  75. 75.
    Heimann T, Meinzer H-P: Statistical shape models for 3d medical image segmentation A review. Med Image Anal 13(4):543–563, 2009PubMedGoogle Scholar
  76. 76.
    Cootes TF, Taylor CJ, Cooper DH, Graham J: Training Models of Shape from Sets of Examples London: Springer, 1992, pp 9–18Google Scholar
  77. 77.
    Nain D, Haker S, Bobick A, Tannenbaum A Shape-driven 3d segmentation using spherical wavelets. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2006, pp 66–74Google Scholar
  78. 78.
    Moussavi F, Heitz G, Amat F, Comolli LR, Koller D, Horowitz M: 3d segmentation of cell boundaries from whole cell cryogenic electron tomography volumes. J Struct Biol 170(1):134–145, 2010PubMedGoogle Scholar
  79. 79.
    Badakhshannoory H, Saeedi P: A model-based validation scheme for organ segmentation in ct scan volumes. IEEE Trans Biomed Eng 58(9):2681–2693, 2011PubMedGoogle Scholar
  80. 80.
    Zhang S: Towards Robust and Effective Shape Prior Modeling: Sparse Shape Composition. New Brunswick: PhD thesis 2012, AAI3502515Google Scholar
  81. 81.
    Zhang S, Huang J, Uzunbas M, Shen T, Delis F, Huang X, Volkow N, Thanos P, Metaxas DN: 3d segmentation of rodent brain structures using hierarchical shape priors and deformable models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2011, pp 611–618Google Scholar
  82. 82.
    Rüegsegger MB, Cuadra Meritxell B, Pica A, Amstutz CA, Rudolph T, Aebersold D, Kowal JH: Statistical modeling of the eye for multimodal treatment planning for external beam radiation therapy of intraocular tumors. Int J Radiat Oncol*Biol*Phys 84(4):e541–e547, 2012PubMedGoogle Scholar
  83. 83.
    Chang Y-B, Xia JJ, Yuan P, Kuo T-H, Xiong Z, Gateno J, Zhou X: 3d segmentation of maxilla in cone-beam computed tomography imaging using base invariant wavelet active shape model on customized two-manifold topology. J X-ray Sci Technol 21(2):251–282, 2013Google Scholar
  84. 84.
    Brice CR, Fennema CL: Scene analysis using regions. Artif Intell 1(3):205–226, 1970Google Scholar
  85. 85.
    Haralick RM, Shapiro LG: Image segmentation techniques. Comput Vis Graph Image Process 29(1):100–132, 1985Google Scholar
  86. 86.
    Freixenet J, Muñoz X, Raba D, Martí J, Cufí X: Yet another survey on image segmentation: Region and boundary information integration. In: Proceedings of the 7th European Conference on Computer Vision-Part III, ECCV ’02. Springer, London, 2002, pp 408–422Google Scholar
  87. 87.
    Bernard Davis J, Reiner B, Huser M, Burger C, Székely G, Frank Ciernik I: Assessment of 18f {PET} signals for automatic target volume definition in radiotherapy treatment planning. Radiother Oncol 80(1):43–50, 2006PubMedGoogle Scholar
  88. 88.
    Staal J, van Ginneken B, Viergever MA: Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data. Med Image Anal 11(1):35–46, 2007PubMedGoogle Scholar
  89. 89.
    Monga O: Defining and computing stable representations of volume shapes from discrete trace using volume primitives: Application to 3d image analysis in soil science. Image Vis Comput 25(7):1134–1153, 2007Google Scholar
  90. 90.
    Bulu H, Alpkocak A: Comparison of 3d segmentation algorithms for medical imaging. In: Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS’07), June 2007, pp 269–274Google Scholar
  91. 91.
    Diciotti S, Picozzi G, Falchini M, Mascalchi M, Villari N, Valli G: 3-d segmentation algorithm of small lung nodules in spiral ct images. IEEE Trans Inf Technol Biomed 12(1):7–19, 2008PubMedGoogle Scholar
  92. 92.
    Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP-Y, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Matilda Jude C, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP: The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Med Phys 38(2):915–931 , 2011PubMedPubMedCentralGoogle Scholar
  93. 93.
    Lai K, Zhao P, Huang Y, Liu J, Wang C, Feng H, Li C: Automatic 3d segmentation of lung airway tree A novel adaptive region growing approach. In: 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009, pp 1–4Google Scholar
  94. 94.
    De Nunzio G, Tommasi E, Agrusti A, Cataldo R, De Mitri I, Favetta M, Maglio S, Massafra A, Quarta M, Torsello M, Zecca I, Bellotti R, Tangaro S, Calvini P, Camarlinghi N, Falaschi F, Cerello P, Oliva P: Automatic lung segmentation in ct images with accurate handling of the hilar region. J Digit Imaging 24(1):11–27, 2011PubMedGoogle Scholar
  95. 95.
    Bert A, Dmitriev I, Agliozzo S, Pietrosemoli N, Mandelkern M, Gallo T, Regge D: An automatic method for colon segmentation in {CT} colonography. Comput Med Imaging Graph 33(4):325–331, 2009PubMedGoogle Scholar
  96. 96.
    Gloger O, Kühn J, Stanski A, Völzke H, Puls R: A fully automatic three-step liver segmentation method on lda-based probability maps for multiple contrast {MR} images. Magn Reson Imaging 28(6):882–897, 2010PubMedGoogle Scholar
  97. 97.
    Ren Yh, Sun Xw, Nie Sd: A 3d segmentation method of lung parenchyma based on ct image sequences. In: International Conference on Information, Networking and Automation (ICINA), vol 2, 2010, pp V2–332–V2–336Google Scholar
  98. 98.
    Uher V, Burget R: Automatic 3d segmentation of human brain images using data-mining techniques. In: 2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012, pp 578–580Google Scholar
  99. 99.
    Andrä H, Combaret N, Dvorkin J, Glatt E, Han J, Kabel M, Keehm Y, Krzikalla F, Lee M, Madonna C, Marsh M, Mukerji T, Saenger EH, Sain R, Saxena N, Ricker S, Wiegmann A, Zhan X: Digital rock physics benchmarks-part i: Imaging and segmentation. Comput Geosci 50:25–32, 2013. Benchmark problems, datasets and methodologies for the computational geosciencesGoogle Scholar
  100. 100.
    Badura P, Pietka E: Soft computing approach to 3d lung nodule segmentation in {CT}. Comput Biol Med 53: 230–243, 2014PubMedGoogle Scholar
  101. 101.
    Werz T, Baumann M, Wolfram U, Krill III CE: Particle tracking during ostwald ripening using time-resolved laboratory x-ray microtomography. Mater Charact 90:185–195, 2014Google Scholar
  102. 102.
    Paulano F, Jiménez JJ, Pulido R: 3d segmentation and labeling of fractured bone from ct images. Vis Comput 30(6-8):939–948, 2014Google Scholar
  103. 103.
    Pal NR, Pal SK: A review on image segmentation techniques. Pattern Recogn 26 (9): 1277–1294, 1993Google Scholar
  104. 104.
    Blanz WE, Gish SL: A connectionist classifier architecture applied to image segmentation. In: [1990] Proceedings, 10th International Conference on Pattern Recognition, volume ii, 1990, pp 272–277Google Scholar
  105. 105.
    Amza, C: A review on neural network–based image segmentation techniques. De Montfort University, Mechanical and Manufacturing Engg, The gateway leicester, LE1 9BH, United Kingdom, 2012, pp 1–23Google Scholar
  106. 106.
    Li S, Fevens T, Krzyźak A, Li S: Automatic clinical image segmentation using pathological modeling, {PCA} and {SVM}. Eng Appl Artif Intell 19(4):403–410, 2006. Recent Advances in Data MiningGoogle Scholar
  107. 107.
    Chang Q, Shi J, Xiao Z: A new 3d segmentation algorithm based on 3d pcnn for lung ct slices. In: 2009 2nd International Conference on Biomedical Engineering and Informatics, 2009, pp 1–5Google Scholar
  108. 108.
    Santos AM, de Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M: Automatic detection of small lung nodules in 3d {CT} data using gaussian mixture models, tsallis entropy and {SVM}. Eng Appl Artif Intell 36: 27–39, 2014Google Scholar
  109. 109.
    Ye ZZ, Yu QZ, Liao M, Zou BJ, Wang XF, Wang W: Liver vessel segmentation based on extreme learning machine. Phys Med 32(5):709–716, 2016Google Scholar
  110. 110.
    Wang S, Mu Z, Liu Z, Liu Z, Gu D, Zang Y, Di D, Gevaert O, Tian J: Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172–183, 2017PubMedPubMedCentralGoogle Scholar
  111. 111.
    Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T, Nachev P, Modat M, Barratt DC, Ourselin S, Jorge Cardoso M, Vercauteren T: Niftynet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113–122, 2018PubMedPubMedCentralGoogle Scholar
  112. 112.
    Chaves R, Ramírez J, Górriz JM: Integrating discretization and association rule-based classification for alzheimer’s disease diagnosis. Expert Syst Appl 40 (5): 1571–1578, 2013Google Scholar
  113. 113.
    Spampinato C, Pino C, Giordano D, Leonardi R: Automatic 3d segmentation of mandible for assessment of facial asymmetry. In: 2012 IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA), 2012, pp 1–4Google Scholar
  114. 114.
    Karvonen T, Uranishi Y, Sakamoto T, Tona Y, Okamoto K, Tamura H, Kuroda T: 3d reconstruction of cochlea using optical coherence tomography. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp 5905–5908Google Scholar
  115. 115.
    Wang Q, Song E, Jin R, Han P, Wang X, Zhou Y, Zeng J: Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques1. Acad Radiol 16(6):678–688, 2009PubMedGoogle Scholar
  116. 116.
    Lloréns R, Naranjo V, López F, Alcaniz M: Jaw tissues segmentation in dental 3d {CT} images using fuzzy-connectedness and morphological processing. Comput Methods Programs Biomed 108(2):832–843, 2012PubMedGoogle Scholar
  117. 117.
    Xu Z, Bagci U, Jonsson C, Jain S, Mollura DJ: Efficient ribcage segmentation from ct scans using shape features. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp 2899–2902Google Scholar
  118. 118.
    Song H, Kang W, Zhang Q, Wang S: Kidney segmentation in ct sequences using skfcm and improved growcut algorithm. BMC Syst Biol 9:1–11, 2015Google Scholar
  119. 119.
    Zhang W, Kim HM: Fully automatic colon segmentation in computed tomography colonography. In: 2016 IEEE International Conference on Signal and Image Processing (ICSIP), 2016, pp 51–55Google Scholar
  120. 120.
    Jang Y, Ho YJ, Hong Y, Cho I, Shim H, Chang H: Geodesic distance algorithm for extracting the ascending aorta from 3d CT images. Comp Math Methods Med 2016:4561979:1–4561979:7, 2016Google Scholar
  121. 121.
    Rusu M, Starosolski Z, Wahle M, Rigort A, Wriggers W: Automated tracing of filaments in 3d electron tomography reconstructions using sculptor and situs. J Struct Biol 178(2):121–128, 2012. Special Issue: Electron TomographyPubMedPubMedCentralGoogle Scholar
  122. 122.
    Gonçalves L, Novo J, Campilho A: Hessian based approaches for 3d lung nodule segmentation. Expert Syst Appl 61:1–15, 2016Google Scholar
  123. 123.
    Fabijańska A, Goclawski J: 3d segmentation of the cerebrospinal fluid from ct brain scans using local histogram similarity map. In: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015, pp 113–118Google Scholar
  124. 124.
    Wan X, Yang F, Yang F, Li X, Xu M, Tian J: Visualization of multiple anatomical structures with explicit isosurface manipulation. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp 4234–4237Google Scholar
  125. 125.
    Biesdorf A, Wörz S, von Tengg-Kobligk H, Rohr K, Schnörr C: 3d segmentation of vessels by incremental implicit polynomial fitting and convex optimization. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, pp 1540–1543Google Scholar
  126. 126.
    Farzaneh N, Soroushmehr SMR, Williamson CA, Jiang C, Srinivasan A, Bapuraj JR, Ward KR, Korley FK, Najarian K: Automated subdural hematoma segmentation for traumatic brain injured (tbi) patients. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp 3069–3072Google Scholar
  127. 127.
    Czabaj MW, Riccio ML, Whitacre WW: Numerical reconstruction of graphite/epoxy composite microstructure based on sub-micron resolution x-ray computed tomography. Compos Sci Technol 105: 174–182, 2014Google Scholar
  128. 128.
    Loss LA, Bebis G, Chang H, Auer M, Sarkar P, Parvin B: Automatic segmentation and quantification of filamentous structures in electron tomography New York: ACM, 2012, pp 170–177Google Scholar
  129. 129.
    Badura P: Virtual bacterium colony in 3d image segmentation. Comput Med Imaging Graph 65: 152–166, 2018. Advances in Biomedical Image ProcessingPubMedGoogle Scholar
  130. 130.
    Ontiveros S, Yagüe JA, Jiménez R, Brosed F: Computer tomography 3d edge detection comparative for metrology applications. Procedia Eng 63:710–719, 2013. The Manufacturing Engineering Society International Conference {MESIC}Google Scholar
  131. 131.
    Rudyanto RD, Kerkstra S, van Rikxoort EM, Fetita C, Brillet P-Y, Lefevre C, Xue W, Zhu X, Liang J, Öksüz Í, Ünay D, Kadipasaogˇlu K, San josé estépar R, Ross JC, Washko GR, Prieto J-C, Hoyos MH, Orkisz M, Meine H, Hüllebrand M, Stöcker C, Mir Fernando L, Naranjo V, Villanueva E, Staring M, Xiao C, Stoel BC, Fabijanska Anna, Smistad Erik, Elster AC, Lindseth F, Foruzan AH, Kiros R, Popuri K, Cobzas D, Jimenez-Carretero D, Santos A, Ledesma-Carbayo MJ, Helmberger M, Urschler M, Pienn M, Bosboom DGH, Campo A, Prokop M, de Jong Pim A, de Solorzano CO, Barrutia AM, van Ginneken B: Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the {VESSEL12} study. Med Image Anal 18(7):1217–1232, 2014PubMedPubMedCentralGoogle Scholar
  132. 132.
    Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS: Automatic 3d pulmonary nodule detection in ct images. Comput Methods Prog Biomed 124(C):91–107, 2016Google Scholar
  133. 133.
    Dice LR: Measures of the amount of ecologic association between species. Ecology 26(3):297–302, 1945Google Scholar
  134. 134.
    Ben-Hur A, Elisseeff A, Guyon I: A stability based method for discovering structure in clustered data Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, 2002, pp 6–17Google Scholar
  135. 135.
    Chang H-H, Zhuang AH, Valentino DJ, Chu W-C: Performance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage 47(1):122–135, 2009PubMedGoogle Scholar
  136. 136.
    Taha AA, Hanbury A: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):29, 2015PubMedPubMedCentralGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Graduate Program in Computer Science - Federal University of Santa CatarinaFlorianopolisBrazil
  2. 2.Image Processing and Computer Graphics Lab - National Brazilian Institute for Digital Convergence - Federal University of Santa CatarinaFlorianopolisBrazil
  3. 3.Department of Computing - Federal University of Santa CatarinaAraranguaBrazil

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