Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar D, Verma A, Sehgal V et al (2007) Neonatal mortality in India
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 273:232–240
Kidokoro H, Anderson PJ, Doyle LW et al (2014) Brain injury and altered brain growth in preterm infants: predictors and prognosis. Pediatrics 134:e444–e453
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 93:636–640
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 23:396–403
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 143:171–179
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 180–187
Di Rocco M, Biancheri R, Rossi A et al (2004) Genetic disorders affecting white matter in the pediatric age. Am J Med Genet 129B:85–93
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 208:431–439
Atkins MS, Mackiewich BT (1998) Fully automatic segmentation of the brain in MRI. IEEE Trans Med Imaging 17:98–107
Ballester MAG, Zisserman AP, Brady M (2002) Estimation of the partial volume effect in MRI. Med Image Anal 6:389–405
Stiles J, Jernigan TL (2010) The basics of brain development. Neuropsychol Rev 20:327–348
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 71:1266
Shi F, Yap P-T, Wu G et al (2011) Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 6:e18746
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 54:2750–2763
Fonov V, Evans A, McKinstry R et al (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47:S102
Altaye M, Holland SK, Wilke M, Gaser C (2008) Infant brain probability templates for MRI segmentation and normalization. Neuroimage 43:721–730
Sanchez CE, Richards JE, Almli CR (2012) Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age. Dev Psychobiol 54:77–91
Akiyama LF, Richards TR, Imada T et al (2013) Age-specific average head template for typically developing 6-month-old infants. PLoS ONE 8:e73821
Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26:405–421
Xue H, Srinivasan L, Jiang S et al (2007) Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 38:461–477
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 39–45
Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320
Gui L, Lisowski R, Faundez T et al (2012) Morphology-driven automatic segmentation of MR images of the neonatal brain. Med Image Anal 16:1565–1579
Yu X, Zhang Y, Lasky RE et al (2010) Comprehensive brain MRI segmentation in high risk preterm newborns. PLoS ONE 5:e13874
Prastawa M, Gilmore JH, Lin W, Gerig G (2005) Automatic segmentation of MR images of the developing newborn brain. Med Image Anal 9:457–466
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 363–365
Kalavathi P, Prasath VBS (2016) Methods on skull stripping of MRI head scan images—a review. J Digit Imaging 29:365–379
Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173
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 18:909–916
Shattuck DW, Leahy RM (2001) Automated graph-based analysis and correction of cortical volume topology. IEEE Trans Med Imaging 20:1167–1177
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 46:1346–1356
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155
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 182:110–122
Leung KK, Barnes J, Modat M et al (2011) Brain MAPS: an automated, accurate and robust brain extraction technique using a template library. Neuroimage 55:1091–1108
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):98
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 9–15
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)
Anbeek P, Vincken KL, Groenendaal F et al (2008) Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging. Pediatr Res 63:158–163
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 86693X
Wang L, Shi F, Yap P-T et al (2012) 4D multi-modality tissue segmentation of serial infant images. PLoS ONE 7:e44596
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 16–12
Klein S, Staring M, Murphy K et al (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29:196–205
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:e59990
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 95–100
Oishi K, Mori S, Donohue PK et al (2011) Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. Neuroimage 56:8–20
Christensen GE, Rabbitt RD, Miller MI (1994) 3D brain mapping using a deformable neuroanatomy. Phys Med Biol 39:609–618
Collins DL, Holmes CJ, Peters TM, Evans AC (1995) Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapp 3:190–208
Makropoulos A, Gousias IS, Ledig C et al (2014) Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans Med Imaging 33:1818–1831
Cardoso MJ, Melbourne A, Kendall GS et al (2011) Adaptive neonate brain segmentation. Med Image Comput Comput Assist Interv 14:378–386
Srhoj-Egekher V, Benders MJNL, Viergever MA, IÅ¡gum I (2013) Automatic neonatal brain tissue segmentation with MRI. In: Ourselin S, Haynor DR (eds) International society for optics and photonics, p 86691K
Heckemann RA, Hajnal JV, Aljabar P et al (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33:115–126
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 23:983–994
Aljabar P, Heckemann RA, Hammers A et al (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46:726–738
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 28:1266–1277
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 23:903–921
Weisenfeld NI, Warfield SK (2009) Automatic segmentation of newborn brain MRI. Neuroimage 47:564–572
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 766–769
Song Z (2008) Statistical tissue segmentation of neonatal brain MR images. Diss
Cardoso MJ, Melbourne A, Kendall GS et al (2013) AdaPT: an adaptive preterm segmentation algorithm for neonatal brain MRI. Neuroimage 65:97–108
Anbeek P, IÅ¡gum I, van Kooij BJM et al (2013) Automatic segmentation of eight tissue classes in neonatal brain MRI. PLoS ONE 8:e81895
Van Leemput K, Maes F, Vandermeulen D et al (2001) Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 20:677–688
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
George, M.M., Kalaivani, S. (2019). A View on Atlas-Based Neonatal Brain MRI Segmentation. In: Gulyás, B., Padmanabhan, P., Fred, A., Kumar, T., Kumar, S. (eds) ICTMI 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-1477-3_16
Download citation
DOI: https://doi.org/10.1007/978-981-13-1477-3_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1476-6
Online ISBN: 978-981-13-1477-3
eBook Packages: EngineeringEngineering (R0)