Principles and methods for automatic and semi-automatic tissue segmentation in MRI data

  • Lei Wang
  • Teodora Chitiboi
  • Hans Meine
  • Matthias Günther
  • Horst K. Hahn
Review Article

Abstract

The development of magnetic resonance imaging (MRI) revolutionized both the medical and scientific worlds. A large variety of MRI options have generated a huge amount of image data to interpret. The investigation of a specific tissue in 3D or 4D MR images can be facilitated by image processing techniques, such as segmentation and registration. In this work, we provide a brief review of the principles and methods that are commonly applied to achieve superior tissue segmentation results in MRI. The impacts of MR image acquisition on segmentation outcome and the principles of selecting and exploiting segmentation techniques tailored for specific tissue identification tasks are discussed. In the end, two exemplary applications, breast and fibroglandular tissue segmentation in MRI and myocardium segmentation in short-axis cine and real-time MRI, are discussed to explain the typical challenges that can be posed in practical segmentation tasks in MRI data. The corresponding solutions that are adopted to deal with these challenges of the two practical segmentation tasks are thoroughly reviewed.

Keywords

MRI Segmentation Non-uniformity correction 

Abbreviations

AAM

Active appearance model

ASM

Active shape model

CAD

Computer-aided diagnosis

DCE-MRI

Dynamic contrast enhanced magnetic resonance imaging

DSC

Dice similarity coefficient

DWI

Diffusion-weighted imaging

EM

Expectation maximization

FCM

Fuzzy c-means

GMM

Gaussian mixture model

JC

Jaccard coefficient

LV

Left ventricle

MRF

Markov random field

MRI

Magnetic resonance imaging

N3

Non-parametric nonuniform normalization

PCA

Principle component analysis

RF

Radio frequency

ROI

Region of interest

RV

Right ventricle

SNR

Signal-to-noise ratio

TE

Echo time

TR

Repetition time

References

  1. 1.
    Robson MD, Gore JC, Constable RT (1997) Measurement of the point spread function in MRI using constant time imaging. Magn Reson Med 38:733–740CrossRefPubMedGoogle Scholar
  2. 2.
    Sugahara T, Korogi Y, Hirai T et al (1997) Comparison of HASTE and segmented-HASTE sequences with a T2-weighted fast spin-echo sequence in the screening evaluation of the brain. Am J Roentgenol 169:1401–1410CrossRefGoogle Scholar
  3. 3.
    Mugler JP, Brookeman JR (1991) Rapid three-dimensional T1-weighted MR imaging with the MP-RAGE sequence. J Magn Reson Imaging 1:561–567CrossRefPubMedGoogle Scholar
  4. 4.
    Brant-Zawadzki M, Gillan GD, Nitz WR (1992) MP RAGE: a three-dimensional, T1-weighted, gradient-echo sequence–initial experience in the brain. Radiology 182:769–775CrossRefPubMedGoogle Scholar
  5. 5.
    Glover GH (1991) Multipoint Dixon technique for water and fat proton and susceptibility imaging. J Magn Reson Imaging 1:521–530CrossRefPubMedGoogle Scholar
  6. 6.
    Dixon WT (1984) Simple proton spectroscopic imaging. Radiology 153:189–194CrossRefPubMedGoogle Scholar
  7. 7.
    Reeder SB, Wen Z, Yu H et al (2004) Multicoil Dixon chemical species separation with an iterative least-squares estimation method. Magn Reson Med 51:35–45CrossRefPubMedGoogle Scholar
  8. 8.
    Wolansky LJ, Finden SG, Chen J et al (1999) Optimization of gray/white matter contrast with fast inversion recovery for myelin suppression: a comparison of fast spin-echo and echo-planar MR imaging sequences. Am J Neuroradiol 20:1653–1657PubMedGoogle Scholar
  9. 9.
    Mani S, Pauly J, Conolly S et al (1997) Background suppression with multiple inversion recovery nulling: applications to projective angiography. Magn Reson Med 37:898–905CrossRefPubMedGoogle Scholar
  10. 10.
    Shattuck DW, Sandor-Leahy SR, Schaper KA et al (2001) Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13:856–876CrossRefPubMedGoogle Scholar
  11. 11.
    Hahn HK, Peitgen HO (2003) IWT-interactive watershed transform: a hierarchical method for efficient interactive and automated segmentation of multidimensional gray-scale images. In: Proceedings of SPIE medical imaging, pp 643–653Google Scholar
  12. 12.
    Roerdink J, Meijster A (2000) The watershed transform: definitions, algorithms and parallelization strategies. Fundam Inform 41:187–228Google Scholar
  13. 13.
    Meine H, Stelldinger P, Köthe U (2009) Pixel approximation errors in common watershed algorithms. In: Proceedings of discrete geometry for computer imagery, LNCS 5810:193–202Google Scholar
  14. 14.
    Simmons A, Tofts PS, Barker GJ, Arridge SR (1994) Sources of intensity nonuniformity in spin-echo images at 1.5-T. Magn Reson Med 32:121–128CrossRefPubMedGoogle Scholar
  15. 15.
    Alecci M, Collins CM, Smith MB, Jezzard P (2001) Radio frequency magnetic field mapping of a 3 Tesla birdcage coil: experimental and theoretical dependence on sample properties. Magn Reson Med 46:379–385CrossRefPubMedGoogle Scholar
  16. 16.
    Vovk U, Pernuš F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26:405–421CrossRefPubMedGoogle Scholar
  17. 17.
    Gispert JD, Reig S, Pascau J et al (2003) Inhomogeneity correction of magnetic resonance images by minimization of intensity overlapping. In: Proceedings of International Conference on Image Process, pp 14–17Google Scholar
  18. 18.
    Gispert JD, Reig S, Pascau J et al (2004) Method for bias field correction of brain T1-weighted magnetic images minimizing segmentation error. Hum Brain Mapp 22:133–144CrossRefPubMedGoogle Scholar
  19. 19.
    Guillemaud R, Brady M (1997) Estimating the bias field of MR images. IEEE Trans Med Imaging 16:238–251CrossRefPubMedGoogle Scholar
  20. 20.
    Tsai C, Manjunath BS, Jagadeesan R (1995) Automated segmentation of brain MR images. Pattern Recognit 28:1825–1837CrossRefGoogle Scholar
  21. 21.
    Rajapakse JC, Kruggel F (1998) Segmentation of MR images with intensity inhomogeneities. Image Vis Comput 16:165–180CrossRefGoogle Scholar
  22. 22.
    Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57CrossRefPubMedGoogle Scholar
  23. 23.
    Pham DL, Prince JL (1999) An adaptive fuzzy C-Means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 20:57–68CrossRefGoogle Scholar
  24. 24.
    Pham DL, Prince JL (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imag 18:737–752CrossRefGoogle Scholar
  25. 25.
    Li C, Huang R, Ding Z et al (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20:2007–2016CrossRefPubMedGoogle Scholar
  26. 26.
    Ivanovska T, Laqua R, Wang L et al (2013) A fast global variational bias field correction method for MR images. In: Proceedings of 8th international symposium on image signal process, pp 667–671Google Scholar
  27. 27.
    Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97CrossRefPubMedGoogle Scholar
  28. 28.
    Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Orel SG, Schnall MD (2001) MR imaging of the breast for the detection, diagnosis, and staging of breast cancer. Radiology 220:13–30CrossRefPubMedGoogle Scholar
  30. 30.
    DeMartini W, Lehman C (2008) A review of current evidence-based clinical applications for breast magnetic resonance imaging. Top Magn Reson Imaging 19:143–150CrossRefPubMedGoogle Scholar
  31. 31.
    Kuhl C (2007) The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 244:356–378CrossRefPubMedGoogle Scholar
  32. 32.
    Lehman CD, Peacock S, DeMartini WB, Chen X (2006) A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity. Am J Roentgenol 187:51–56CrossRefGoogle Scholar
  33. 33.
    Goto M, Ito H, Akazawa K et al (2007) Diagnosis of breast tumors by contrast-enhanced MR imaging: comparison between the diagnostic performance of dynamic enhancement patterns and morphologic features. J Magn Reson Imaging 25:104–112CrossRefPubMedGoogle Scholar
  34. 34.
    Wu S, Weinstein SP, Conant EF et al (2013) Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images. Med Phys 40:042301CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Milenković J, Chambers O, Marolt Mušič M, Tasič JF (2015) Automated breast-region segmentation in the axial breast MR images. Comput Biol Med 62:55–64CrossRefPubMedGoogle Scholar
  36. 36.
    Giannini V, Vignati A, Morra L et al (2010) A fully automatic algorithm for segmentation of the breasts in DCE-MR images. In: Proceedings of annual international conference on IEEE engineering medicine and biology society EMBC 2010, pp 3146–3149Google Scholar
  37. 37.
    Wang L, Platel B, Ivanovskaya T et al (2012) Fully automatic breast segmentation in 3D breast MRI. In: Proceedings on IEEE international symposium biomedical imaging, pp 1024–1027Google Scholar
  38. 38.
    Wang L, Filippatos K, Friman O, Hahn HK (2011) Fully automated segmentation of the pectoralis muscle boundary in breast MR images. In: Proceedings of SPIE medical imaging, pp 796309–796309–8Google Scholar
  39. 39.
    Koenig M, Laue H, Boehler T, Peitgen H-O (2007) Automatic segmentation of relevant structures in DCE MR mammograms. In: Proceedings on SPIE medical imaging, pp 65141S–65141S–6Google Scholar
  40. 40.
    Nie K, Chen JH, Chan S et al (2008) Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys 35:5253–5262CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Lin M, Chen JH, Wang X et al (2013) Template-based automatic breast segmentation on MRI by excluding the chest region. Med Phys 40:122301CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Ivanovska T, Laqua R, Wang L et al (2014) A level set based framework for quantitative evaluation of breast tissue density from MRI data. PLoS One 9:e112709CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Ortiz CG, Martel AL (2012) Automatic atlas-based segmentation of the breast in MRI for 3D breast volume computation. Med Phys 39:5835CrossRefPubMedGoogle Scholar
  44. 44.
    Khalvati F, Gallego-Ortiz C, Balasingham S, Martel AL (2015) Automated segmentation of breast in 3-D MR images using a robust atlas. IEEE Trans Med Imaging 34:116–125CrossRefPubMedGoogle Scholar
  45. 45.
    Gubern-Mérida A, Kallenberg M, Mann RM et al (2013) Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Health Inform 19:349–357CrossRefGoogle Scholar
  46. 46.
    McCormack VA, dos Santos Silva I (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15:1159–1169CrossRefPubMedGoogle Scholar
  47. 47.
    Wu S, Weinstein S, Conant E, Kontos D (2013) Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method. Med Phys 40:122302CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Razavi M, Wang L, Gubern-Mérida A et al (2015) Towards accurate segmentation of fibroglandular tissue in breast MRI using fuzzy c-means and skin-folds removal. In: Proceedings on 18th international conference image analysis processGoogle Scholar
  49. 49.
    Petitjean C, Dacher JN (2011) A review of segmentation methods in short axis cardiac MR images. Med Image Anal 15:169–184CrossRefPubMedGoogle Scholar
  50. 50.
    Katouzian A, Prakash A, Konofagou E (2006) A new automated technique for left-and right-ventricular segmentation in magnetic resonance imaging. In: Conference proceedings IEEE engineering in medicine and biology society, pp 3074–3077Google Scholar
  51. 51.
    Heckel F, Meine H, Moltz JH et al (2014) Segmentation-based partial volume correction for volume estimation of solid lesions in CT. IEEE Trans Med Imaging 33(2):462–480CrossRefPubMedGoogle Scholar
  52. 52.
    Kurkure U, Pednekar A, Muthupillai R et al (2009) Localization and segmentation of left ventricle in cardiac cine-MR images. IEEE Trans Biomed Eng 56:1360–1370CrossRefPubMedGoogle Scholar
  53. 53.
    Lynch M, Ghita O, Whelan PF (2006) Automatic segmentation of the left ventricle cavity and myocardium in MRI data. Comput Biol Med 36:289–407CrossRefGoogle Scholar
  54. 54.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vision 1:321–331CrossRefGoogle Scholar
  55. 55.
    Xu C, Prince JL, Hall B (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7:359–369CrossRefPubMedGoogle Scholar
  56. 56.
    Heimann T, Meinzer H-P (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13:543–563CrossRefPubMedGoogle Scholar
  57. 57.
    Geiger D (1996) Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Trans Pattern Anal Mach Intell 17:294–302CrossRefGoogle Scholar
  58. 58.
    Lynch M, Ghita O, Whelan PF (2008) Segmentation of the left ventricle of the heart in 3-D + t MRI data using an optimized nonrigid temporal model. IEEE Trans Med Imaging 27:195–203CrossRefPubMedGoogle Scholar
  59. 59.
    Cousty J, Najman L, Couprie M et al (2010) Segmentation of 4D cardiac MRI automated method based on spatio-temporal watershed cuts. Image Vis Comput 28:1229–1243CrossRefGoogle Scholar
  60. 60.
    Jolly M-P (2006) Automatic segmentation of the left ventricle in cardiac MR and CT images. Int J Comput Vis 70:151–163CrossRefGoogle Scholar
  61. 61.
    Kedenburg G, Cocosco CA, Köthe U et al (2006) Automatic cardiac MRI myocardium segmentation using graphcut. In: Proceedings of SPIE, p 61440A–61440A–12Google Scholar
  62. 62.
    Uzümcü M, van der Geest RJ, Swingen C et al (2006) Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming. Invest Radiol 41:52–62CrossRefPubMedGoogle Scholar
  63. 63.
    Üzümcü M, Frangi AF, Sonka M et al (2003) ICA vs. PCA active appearance models: application to cardiac MR segmentation. In: Medical image computing and computer-assisted intervention—MICCAI 2003, pp 451–458Google Scholar
  64. 64.
    Mitchell SC, Lelieveldt BP, Van der Geest RJ et al (2001) Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 20:415–423CrossRefPubMedGoogle Scholar
  65. 65.
    Zhuang X, Hawkes D, Crum W et al (2008) Robust registration between cardiac MRI images and atlas for segmentation propagation. In: SPIE Medical Imaging, pp 691408Google Scholar
  66. 66.
    Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D (2002) Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In: Int. Conf. Med. Image Comput. Comput. Interv. Springer, pp 642–650Google Scholar
  67. 67.
    Cocosco CA, Netsch T, Sénégas J et al (2004) Automatic cardiac region-of-interest computation in cine 3D structural MRI. In: Int. Congr. Ser. pp 1126–1131Google Scholar
  68. 68.
    Hüllebrand M, Hennemuth A, Messroghli D et al (2011) Semi-automatic 4D fuzzy connectedness segmentation of heart ventricles in cine MRI. In: Bild. für die Medizin 2011. Springer, pp 3–7Google Scholar
  69. 69.
    Lee HY, Codella NCF, Cham MD et al (2010) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEEE Trans Biomed Eng 57:905–913CrossRefPubMedGoogle Scholar
  70. 70.
    Queirós S, Barbosa D, Heyde B et al (2014) Fast automatic myocardial segmentation in 4D cine CMR datasets. Med Image Anal 18:1115–1131CrossRefPubMedGoogle Scholar
  71. 71.
    Gotardo PFU, Boyer KL, Saltz J, Raman SV (2006) A new deformable model for boundary tracking in cardiac MRI and its application to the detection of intra-ventricular dyssynchrony. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp 736–743Google Scholar
  72. 72.
    Jolly M-P (2008) Automatic recovery of the left ventricular blood pool in cardiac cine MR images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008:110–118Google Scholar
  73. 73.
    Pednekar A, Kurkure U, Muthupillai R, Flamm S, Kakadiaris I (2006) Automated left ventricular segmentation in cardiac MRI. IEEE Trans Biomed Eng 53:1425–1428CrossRefPubMedGoogle Scholar
  74. 74.
    Yip RKK, Tam PKS, Leung DNK (1995) Modification of Hough transform for object recognition using a 2-dimensional array. Pattern Recognit 28:1733–1744CrossRefGoogle Scholar
  75. 75.
    Petitjean C, Zuluaga MA, Bai W et al (2015) Right ventricle segmentation from cardiac MRI: a collation study. Med Image Anal 19:187–202CrossRefPubMedGoogle Scholar
  76. 76.
    Ou Y, Doshi J, Erus G, Davatzikos C (2012) Multi-atlas segmentation of the cardiac MR right ventricle. Proceedings of 3D Cardiovascular Imaging. In: Proceedings of 3D Cardiovascular Imaging: A MICCAI Segmentation Challenge, Nice, FranceGoogle Scholar
  77. 77.
    Zuluaga MA, Cardoso MJ, Modat M, Ourselin S (2013) Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion. In: Proc. Functional Imaging and Modeling of the Heart LNCS 7945:174–181Google Scholar
  78. 78.
    Bai W, Shi W, Wang H et al (2012) Multi-Atlas Based Segmentation with Local Label Fusion for Right Ventricle MR Images. Work Med Image Comput Comput Assist Interv 1–8Google Scholar
  79. 79.
    Grosgeorge D, Petitjean C, Dacher JN, Ruan S (2013) Graph cut segmentation with a statistical shape model in cardiac MRI. Comput Vis Image Underst 117:1027–1035CrossRefGoogle Scholar
  80. 80.
    Mahapatra D, Buhmann JM (2013) Automatic cardiac RV segmentation using semantic information with graph cuts. In: Proc. Int. Symp. Biomed. Imaging. IEEE, pp 1106–1109Google Scholar
  81. 81.
    Uecker M, Zhang S, Voit D et al (2010) Real-time MRI at a resolution of 20 ms. NMR Biomed 23:986–994CrossRefPubMedGoogle Scholar
  82. 82.
    Voit D, Zhang S, Unterberg-Buchwald C et al (2013) Real-time cardiovascular magnetic resonance at 1.5 T using balanced SSFP and 40 ms resolution. J Cardiovasc Magn Reson 15:79CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Chan YT, Hu AGC, Plant JB (1979) A Kalman Filter Based Tracking Scheme with Input Estimation. IEEE Trans Aerosp Electron Syst AES-15:237-244Google Scholar
  84. 84.
    Achanta R, Shaji A, Smith K et al (2010) SLIC Superpixels. EPFL Tech Rep 149300:15Google Scholar
  85. 85.
    Homeyer A, Schwier M, Hahn HK (2010) A generic concept for object-based image analysis. In: Proc. Int. Conf. Comput. Vis. Theory Appl. pp 530–533Google Scholar
  86. 86.
    Chitiboi T, Hennemuth A (2014) Context-based segmentation and analysis of multi-cycle real-time cardiac MRI. In: Proc. IEEE Int. Symp. Biomed. Imaging. pp 943–946Google Scholar
  87. 87.
    Tautz L, Hennemuth A, Andersson M et al (2010) Phase-based non-rigid registration of myocardial perfusion MR image sequences. In: Proc. IEEE Int. Symp. Biomed. Imaging. pp 516–519Google Scholar
  88. 88.
    Hüllebrand M, Hennemuth A, Messroghli D, Kühne T (2014) OsiriX plugin for integrated cardiac imaging research. In: Proc. SPIE Medical Imaging, pp 90390D-90390DGoogle Scholar
  89. 89.
    Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28:1768–1783CrossRefPubMedGoogle Scholar
  90. 90.
    Eslami A, Karamalis A, Katouzian A, Navab N (2013) Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI. Med Image Anal 17:236–253CrossRefPubMedGoogle Scholar
  91. 91.
    Wu S, Weinstein SP, Conant EF, Kontos D (2013) Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas. In: Proc. SPIE Medical Imaging, pp 8670:86701C–86701C–6Google Scholar
  92. 92.
    Gubern-Mérida A, Martí R, Melendez J et al (2014) Automated localization of breast cancer in DCE-MRI. Med Image Anal 20:265–274CrossRefPubMedGoogle Scholar
  93. 93.
    Pang Z, Zhu D, Chen D et al (2015) A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection. Comput Math Methods Med 2015:450531CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Ertas G, Gulcur HO, Osman O et al (2008) Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Comput Biol Med 38:116–126CrossRefPubMedGoogle Scholar
  95. 95.
    Luthi M, Blanc R, Albrecht T et al (2012) Statismo-A framework for PCA based statistical models. Insight Journal 2012:1–18Google Scholar
  96. 96.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern Syst 9:62–66CrossRefGoogle Scholar
  97. 97.
    Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24:603–619CrossRefGoogle Scholar
  98. 98.
    Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imagingGoogle Scholar
  99. 99.
    Kikinis R, Shenton ME, Iosifescu DV et al (1996) A digital brain atlas for surgical planning, model-driven segmentation, and teaching. IEEE Trans Vis Comput Graph 2:232–241CrossRefGoogle Scholar
  100. 100.
    Rohlfing T, Brandt R, Menzel R et al (2005) Quo vadis, atlas-based segmentation? Handbook of Biomedical Image Analysis, pp 435–486Google Scholar
  101. 101.
    Rohlfing T, Brandt R, Menzel R, Maurer CR (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21:1428–1442CrossRefPubMedGoogle Scholar
  102. 102.
    Osher S, Sethian J (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79:12–49CrossRefGoogle Scholar
  103. 103.
    Sethian J (1999) Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge University Press, CambridgeGoogle Scholar
  104. 104.
    Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. Pattern Anal Mach Intell IEEE Trans 26:1124–1137CrossRefGoogle Scholar
  105. 105.
    Kolmogorov V, Zabin R (2004) What energy functions can be minimized via graph cuts? Pattern Anal Mach Intell IEEE Trans 26:147–159CrossRefGoogle Scholar
  106. 106.
    Li SZ (2009) Markov random field modeling in image analysis. Springer Science & Business Media, BerlinGoogle Scholar
  107. 107.
    Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–921CrossRefPubMedPubMedCentralGoogle Scholar
  108. 108.
    Dice LR (1945) Measures of the Amount of Ecologic Association Between Species. Ecology 26(3):297–302CrossRefGoogle Scholar
  109. 109.
    Heimann T, van Ginneken B, Styner MA et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251–1265CrossRefPubMedGoogle Scholar
  110. 110.
    Deng X, Zhu L, Sun Y et al (2007) On simulating subjective evaluation using combined objective metrics for validation of 3D tumor segmentation. Med Image Comput Comput Assist Interv 10:977–984PubMedGoogle Scholar

Copyright information

© ESMRMB 2016

Authors and Affiliations

  1. 1.Institute for Medical Image ComputingFraunhofer MEVIS Universitaetsallee 29BremenGermany

Personalised recommendations