ICTMI 2017 pp 199-214 | Cite as

A View on Atlas-Based Neonatal Brain MRI Segmentation

  • Maryjo M. George
  • S. KalaivaniEmail author
Conference paper


Purpose This paper provides an outline of the atlas-based algorithms in the area of neonatal brain MRI segmentation. The goal is to provide a complete overview of the existing atlas-based segmentation strategies and evaluation methods. Procedures Pros and cons of commonly used neuroimaging techniques are analyzed. A detailed review of each stage in the automatic delineation process is presented here. Preprocessing, registration, label propagation, and fusion methods used in various atlas-based approaches are discussed. Furthermore, the validation schemes are also addressed. Results The findings in this paper prefer MRI to be most suitable imaging modality in case of neonatal brain analysis. The atlas-based segmentation strategies can be categorized into two: multiple atlas and probabilistic atlas-based methods. A detailed comparison of both the methods is provided. Conclusion This paper reviews recent and prominent atlas-based segmentation algorithms for neonatal brain MRI. The major challenges and future directions in this area are also identified in this paper.


Atlas-based segmentation algorithms Brain tissue classification Magnetic resonance imaging Neonatal brain Segmentation algorithm evaluation 


  1. 1.
    Kumar D, Verma A, Sehgal V et al (2007) Neonatal mortality in IndiaGoogle Scholar
  2. 2.
    Walsh JM, Doyle LW, Anderson PJ et al (2014) Moderate and late preterm birth: effect on brain size and maturation at term-equivalent age. Radiology 273:232–240CrossRefGoogle Scholar
  3. 3.
    Kidokoro H, Anderson PJ, Doyle LW et al (2014) Brain injury and altered brain growth in preterm infants: predictors and prognosis. Pediatrics 134:e444–e453CrossRefGoogle Scholar
  4. 4.
    Robertson C, Sauve RS, Christianson HE et al (1994) Province-based study of neurologic disability among survivors weighing 500 through 1249 grams at birth. Pediatrics 93:636–640Google Scholar
  5. 5.
    Miall Lawrence S, Cornette Luc G, Tanner Steven F, Arthur Rosemary J, Levene MI (2003) Posterior fossa abnormalities seen on magnetic resonance brain imaging in a cohort of newborn infants—ProQuest. J Perinatol 23:396–403CrossRefGoogle Scholar
  6. 6.
    Inder TE, Wells SJ, Mogridge NB, Spencer C, Volpe JJ (2003) Defining the nature of the cerebral abnormalities in the premature infant: a qualitative magnetic resonance imaging study. J Pediatr 143:171–179CrossRefGoogle Scholar
  7. 7.
    Aubert-Broche B, Fonov V, Leppert I et al Human brain myelination from birth to 4.5 years. In: Medical image computing and computer-assisted intervention—MICCAI 2008. Springer, Berlin, pp 180–187CrossRefGoogle Scholar
  8. 8.
    Di Rocco M, Biancheri R, Rossi A et al (2004) Genetic disorders affecting white matter in the pediatric age. Am J Med Genet 129B:85–93CrossRefGoogle Scholar
  9. 9.
    Kitagaki H, Mori E, Yamaji S et al (1998) Frontotemporal dementia and Alzheimer disease: evaluation of cortical atrophy with automated hemispheric surface display generated with MR images. Radiology 208:431–439CrossRefGoogle Scholar
  10. 10.
    Atkins MS, Mackiewich BT (1998) Fully automatic segmentation of the brain in MRI. IEEE Trans Med Imaging 17:98–107CrossRefGoogle Scholar
  11. 11.
    Ballester MAG, Zisserman AP, Brady M (2002) Estimation of the partial volume effect in MRI. Med Image Anal 6:389–405CrossRefGoogle Scholar
  12. 12.
    Stiles J, Jernigan TL (2010) The basics of brain development. Neuropsychol Rev 20:327–348CrossRefGoogle Scholar
  13. 13.
    Holland D, Chang L, Ernst TM et al (2014) Structural growth trajectories and rates of change in the first 3 months of infant brain development. JAMA Neurol 71:1266CrossRefGoogle Scholar
  14. 14.
    Shi F, Yap P-T, Wu G et al (2011) Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 6:e18746CrossRefGoogle Scholar
  15. 15.
    Kuklisova-Murgasova M, Aljaba P, Srinivasan L, Counsell SJ, Doria V, Serag Ahmed et al (2011) A dynamic 4D probabilistic atlas of the developing brain. Neuroimage 54:2750–2763CrossRefGoogle Scholar
  16. 16.
    Fonov V, Evans A, McKinstry R et al (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47:S102CrossRefGoogle Scholar
  17. 17.
    Altaye M, Holland SK, Wilke M, Gaser C (2008) Infant brain probability templates for MRI segmentation and normalization. Neuroimage 43:721–730CrossRefGoogle Scholar
  18. 18.
    Sanchez CE, Richards JE, Almli CR (2012) Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age. Dev Psychobiol 54:77–91CrossRefGoogle Scholar
  19. 19.
    Akiyama LF, Richards TR, Imada T et al (2013) Age-specific average head template for typically developing 6-month-old infants. PLoS ONE 8:e73821CrossRefGoogle Scholar
  20. 20.
    Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26:405–421CrossRefGoogle Scholar
  21. 21.
    Xue H, Srinivasan L, Jiang S et al (2007) Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 38:461–477CrossRefGoogle Scholar
  22. 22.
    Shi F, Yap P-T, Fan Y et al (2009) Cortical enhanced tissue segmentation of neonatal brain MR images acquired by a dedicated phased array coil. In: 2009 IEEE computer society conference on computer vision pattern recognition work. IEEE, New York, pp 39–45Google Scholar
  23. 23.
    Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRefGoogle Scholar
  24. 24.
    Gui L, Lisowski R, Faundez T et al (2012) Morphology-driven automatic segmentation of MR images of the neonatal brain. Med Image Anal 16:1565–1579CrossRefGoogle Scholar
  25. 25.
    Yu X, Zhang Y, Lasky RE et al (2010) Comprehensive brain MRI segmentation in high risk preterm newborns. PLoS ONE 5:e13874CrossRefGoogle Scholar
  26. 26.
    Prastawa M, Gilmore JH, Lin W, Gerig G (2005) Automatic segmentation of MR images of the developing newborn brain. Med Image Anal 9:457–466CrossRefGoogle Scholar
  27. 27.
    Vaishali S, Rao KK, Rao GVS (2015) A review on noise reduction methods for brain MRI images. In: 2015 International conference on signal process communication engineering systems. IEEE, New York, pp 363–365Google Scholar
  28. 28.
    Kalavathi P, Prasath VBS (2016) Methods on skull stripping of MRI head scan images—a review. J Digit Imaging 29:365–379CrossRefGoogle Scholar
  29. 29.
    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173CrossRefGoogle Scholar
  30. 30.
    Dawant BM, Hartmann SL, Thirion J-P et al (1999) Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects. IEEE Trans Med Imaging 18:909–916CrossRefGoogle Scholar
  31. 31.
    Shattuck DW, Leahy RM (2001) Automated graph-based analysis and correction of cortical volume topology. IEEE Trans Med Imaging 20:1167–1177CrossRefGoogle Scholar
  32. 32.
    Aboutanos GB, Nikanne J, Watkins N, Dawant BM (1999) Model creation and deformation for the automatic segmentation of the brain in MR images. IEEE Trans Biomed Eng 46:1346–1356CrossRefGoogle Scholar
  33. 33.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155CrossRefGoogle Scholar
  34. 34.
    Merisaari H, Parkkola R, Alhoniemi E et al (2009) Gaussian mixture model-based segmentation of MR images taken from premature infant brains. J Neurosci Methods 182:110–122CrossRefGoogle Scholar
  35. 35.
    Leung KK, Barnes J, Modat M et al (2011) Brain MAPS: an automated, accurate and robust brain extraction technique using a template library. Neuroimage 55:1091–1108CrossRefGoogle Scholar
  36. 36.
    Cointepas Y, Mangin J-F, Garnero L et al (2001) BrainVISA: software platform for visualization and analysis of multi-modality brain data. Neuroimage 6(Supplement):98CrossRefGoogle Scholar
  37. 37.
    Makropoulos A, Ledig C, Aljabar P et al (2012) Automatic tissue and structural segmentation of neonatal brain MRI using Expectation-Maximization. In: MICCAI grand challenge: neonatal brain segmentation (NeoBrainS12), pp 9–15Google Scholar
  38. 38.
    Srhoj-Egekher V, Benders MJNL, Kersbergen KJ et al (2012) Automatic segmentation of neonatal brain MRI using atlas based segmentation and machine learning approach. In: MICCAI grand challenge: neonatal brain segmentation (NeoBrainS12)Google Scholar
  39. 39.
    Anbeek P, Vincken KL, Groenendaal F et al (2008) Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging. Pediatr Res 63:158–163CrossRefGoogle Scholar
  40. 40.
    Chiţă SM, Benders M, Moeskops P et al (2013) Automatic segmentation of the preterm neonatal brain with MRI using supervised classification. In: Ourselin S, Haynor DR (eds) International society for optics and photonics, p 86693XGoogle Scholar
  41. 41.
    Wang L, Shi F, Yap P-T et al (2012) 4D multi-modality tissue segmentation of serial infant images. PLoS ONE 7:e44596CrossRefGoogle Scholar
  42. 42.
    Melbourne A, Cardoso MJ, Kendall GS, Robertson NJ, Neil M, Sebastien O (2012) NeoBrainS12 challenge: adaptive neonatal MRI brain segmentation with myelinated white matter class and automated extraction of ventricles I-IV. In: MICCAI grand challenge: neonatal brain segmentation (NeoBrainSI2), pp 16–12Google Scholar
  43. 43.
    Klein S, Staring M, Murphy K et al (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29:196–205CrossRefGoogle Scholar
  44. 44.
    Gousias IS, Hammers A, Counsell SJ et al (2013) Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions. PLoS ONE 8:e59990CrossRefGoogle Scholar
  45. 45.
    Gousias IS, Hammers A, Counsell SJ et al (2012) Automatic segmentation of pediatric brain MRIs using a maximum probability pediatric atlas. In: 2012 IEEE international conference on imaging systems technology process. IEEE, New York, pp 95–100Google Scholar
  46. 46.
    Oishi K, Mori S, Donohue PK et al (2011) Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. Neuroimage 56:8–20CrossRefGoogle Scholar
  47. 47.
    Christensen GE, Rabbitt RD, Miller MI (1994) 3D brain mapping using a deformable neuroanatomy. Phys Med Biol 39:609–618CrossRefGoogle Scholar
  48. 48.
    Collins DL, Holmes CJ, Peters TM, Evans AC (1995) Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapp 3:190–208CrossRefGoogle Scholar
  49. 49.
    Makropoulos A, Gousias IS, Ledig C et al (2014) Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans Med Imaging 33:1818–1831CrossRefGoogle Scholar
  50. 50.
    Cardoso MJ, Melbourne A, Kendall GS et al (2011) Adaptive neonate brain segmentation. Med Image Comput Comput Assist Interv 14:378–386Google Scholar
  51. 51.
    Srhoj-Egekher V, Benders MJNL, Viergever MA, Išgum I (2013) Automatic neonatal brain tissue segmentation with MRI. In: Ourselin S, Haynor DR (eds) International society for optics and photonics, p 86691KGoogle Scholar
  52. 52.
    Heckemann RA, Hajnal JV, Aljabar P et al (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33:115–126CrossRefGoogle Scholar
  53. 53.
    Rohlfing T, Russakoff DB, Maurer CR (2004) Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans Med Imaging 23:983–994CrossRefGoogle Scholar
  54. 54.
    Aljabar P, Heckemann RA, Hammers A et al (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46:726–738CrossRefGoogle Scholar
  55. 55.
    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 28:1266–1277CrossRefGoogle Scholar
  56. 56.
    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 23:903–921CrossRefGoogle Scholar
  57. 57.
    Weisenfeld NI, Warfield SK (2009) Automatic segmentation of newborn brain MRI. Neuroimage 47:564–572CrossRefGoogle Scholar
  58. 58.
    Weisenfeld NI, Mewes AUJ, Warfield SK Segmentation of Newborn Brain MRI. In: 3rd IEEE international symposium on biomedical imaging macro to nano, 2006. IEEE, New York, pp 766–769Google Scholar
  59. 59.
    Song Z (2008) Statistical tissue segmentation of neonatal brain MR images. DissGoogle Scholar
  60. 60.
    Cardoso MJ, Melbourne A, Kendall GS et al (2013) AdaPT: an adaptive preterm segmentation algorithm for neonatal brain MRI. Neuroimage 65:97–108CrossRefGoogle Scholar
  61. 61.
    Anbeek P, Išgum I, van Kooij BJM et al (2013) Automatic segmentation of eight tissue classes in neonatal brain MRI. PLoS ONE 8:e81895CrossRefGoogle Scholar
  62. 62.
    Van Leemput K, Maes F, Vandermeulen D et al (2001) Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 20:677–688CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Communication Engineering, SENSEVIT UniversityVelloreIndia

Personalised recommendations