A comparative study and overview on the magnetic resonance images skull stripping methods and their correspondence techniques

Document Type : Research Paper

Authors

Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

Abstract

It is crucial to remember that the brain is a part of the body responsible for a wide range of sophisticated bodily activities. Brain imaging can be used to diagnose a wide range of brain problems, including brain tumours, strokes, paralysis, and other neurological conditions. An imaging technique known as Magnetic Resonance Imaging (MRI) is a relatively new method that can classify and categorize the brain non-brain tissues through high-resolution imaging's. For automated brain picture segmentation and analysis, the existence of these non-brain tissues is seen as a critical roadblock to success. For quantitative morphometric examinations of MR brain images, skull-stripping is often required. Skull-stripping procedures are described in this work, as well as a summary of the most recent research on skull-stripping.

Keywords

[1] S. Ahmed, K.Y. Choi, J.J. Lee, B.C. Kim, G.-R. Kwon, K.H. Lee and H.Y. Jung, Ensembles of patch-based
classifiers for diagnosis of alzheimer diseases, IEEE Access 7 (2019) 73373–73383.[2] S.A. Al-Majeed and M.S.H. Al-Tamimi, Survey based study: Classification of patients with alzheimer’s disease,
Iraqi J. Sci. 61(11) (2020) 3104–3126.
[3] M.S.H. Al-Tamimi, A.S.H. Al-Tamimi and G. Sulong, A new abnormality detection approach for T1-weighted
magnetic resonance imaging brain slices using three planes, Adv. Comput. 6(1) (2016) 6–27.
[4] M.S.H. Al-Tamimi and G. Sulong, A review of snake models in medical MR image segmentation, J. Teknol. (Sci.
Eng.) 69(2) (2014) 101–106.
[5] M.S.H. Al-Tamimi and G. Sulong, A new method for detecting cerebral tissues abnormality in magnetic resonance
images, Mod. Appl. Sci. 9(8) (2015) 363–379.
[6] M.S.H. Al-Tamimi, G. Sulong and I.L. Shuaib, Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images, Magn. Reson. Imag. 33(6) (2015) 787–803.
[7] J. Ashburner and K.J. Friston, Voxel-based morphometry - the methods, Neuroimage 11(6I) (2000) 805–821.
[8] M.S. Atkins and B.T. Mackiewich, Fully automatic segmentation of the brain in MRI, IEEE Trans. Med. Imag.
17(1) (1998) 98–107.
[9] M.S. Atkins, K. Siu, B. Law, J.J. Orchard and W.L. Rosenbaum, Difficulties of T1 brain MRI segmentation
techniques, Med. Imag. 2002 Image Process 4684(2002) (2002) 1837.
[10] V. Badrinarayanan, A. Kendall and R. Cipolla, SegNet: A deep convolutional encoder-decoder architecture for
image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39(12) (2017) 2481–2495.
[11] A.G.R. Balan, A.J.M. Traina, M.X. Ribeiro, P.M.A. Marques and C. Traina, Smart histogram analysis applied
to the skull-stripping problem in T1-weighted MRI, Comput. Biol. Med. 42(5) (2012) 509–522.
[12] S. Bao and A.C.S. Chung, Multi-scale structured CNN with label consistency for brain MR image segmentation,
Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(1) (2018) 113–117.
[13] S. Bauer, T. Fejes and M. Reyes, A skull-stripping filter for ITK, Insight J. (2012) 1–7.
[14] S. Bauer, L.-P. Nolte and M. Reyes, Skull-stripping for tumor-bearing brain images, Annual Meeting of the Swiss
Society for Biomedical Engineering, 2012.
[15] A. Beers, J. Brown, K. Chang, K. Hoebel, J. Patel, K.I. Ly, S.M. Tolaney, P. Brastianos, B. Rosen, E.R. Gerstner
and J. Kalpathy-Cramer, Deepneuro: An open-source deep learning toolbox for neuroimaging, Neuroinformatics
19(1) (2021) 127–140.
[16] C.C. Benson and V.L. Lajish, Morphology based enhancement and skull stripping of MRI brain images, Proc.
2014 Int. Conf. Intell. Comput. Appl. ICICA 2014 (2014) 254–257.
[17] J. Bernal, K. Kushibar, M. Cabezas, S. Valverde, A. Oliver and X. Llado, Quantitative analysis of patch-based
fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging, IEEE Access 7
(2019) 89986–90002.
[18] A.S. Bhadauria, V. Bhateja, M. Nigam and A. Arya, Skull stripping of brain MRI using mathematical morphology,
Proc. Second Int. Conf. SCI 1 (2018) 775–780.
[19] M.E. Brummer, R.M. Mersereau, R.L. Eisner and R.R.J. Lewine, Automatic detection of brain contours in MRI
data sets, Lect. Notes Comput. Sci. 511(2) (1991) 189–204.
[20] A. Carass, J. Cuzzocreo, M.B. Wheeler, P.L. Bazin, S.M. Resnick and J.L. Prince, Simple paradigm for extracerebral tissue removal: Algorithm and analysis, Neuroimage 56(4) (2011) 1982–1992.
[21] D. Carmo, B. Silva, C. Yasuda, L. Rittner and R. Lotufo, Extended 2D consensus hippocampus segmentation,
arXiv preprint arXiv:1902.04487, (2019).
[22] C.C. Chen, H.-C. Chen, H.C. Wang, Y.-C. Chang, Y.-Y. Wu, W.-H. Chen, H.-M. Chen, S.-K. Lee and C.-I. Chang,
An iterative mixed pixel classification for brain tissues and white matter hyperintensity in magnetic resonance
imaging, IEEE Access 7 (2019) 124674–124687.
[23] H. Chen, Q. Dou, L. Yu, J. Qin and P.A. Heng, Voxresnet: Deep voxelwise residual networks for brain segmentation
from 3D MR images, Neuroimage 170 (2018) 446–455.
[24] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A.L. Yuille, Semantic image segmentation with deep
convolutional nets and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell. 40(4) (2015).
[25] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A.L. Yuille, Deeplab: semantic image segmentation with
deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell.
40(4) (2017) 834–848.
[26] L.-C. Chen, G. Papandreou, F. Schroff and H. Adam, Encoder-decoder with atrous separable convolution for
semantic image segmentation, Pertanika J. Trop. Agric. Sci. 34(1) (2011) 137–143.
[27] L.-C. Chen, G. Papandreou, F. Schroff and H. Adam, Rethinking atrous convolution for semantic image segmentation, arXiv preprint arXiv:1706.05587, (2017).
[28] K. Chen, J.-S. Shen, F. Scalzo, Skull stripping using confidence segmentation convolution neural network, International Symposium on Visual Computing, Springer, Cham. (2018) 15–24.[29] J. Chiverton, K. Wells, E. Lewis, C. Chen, B. Podda and D. Johnson, Statistical morphological skull stripping of
adult and infant MRI data, Comput. Biol. Med. 37(3) (2007) 342–357.
[30] T.F. Cootes, C.J. Taylor, D.H. Cooper and J. Graham, Active shape models–their training and application,
Computer Vision and Image Understand. 61(1) (1995) 38–59.
[31] P. Coup´e, J.V. Manj´on, V. Fonov, J. Pruessner, M. Robles and D.L. Collins, Patch-based segmentation using
expert priors: Application to hippocampus and ventricle segmentation, Neuroimage 54(2) (2011) 940–954.
[32] O. C¸ i¸cek, A. Abdulkadir, S.S. Lienkamp, T. Brox and O. Ronneberger, ¨ 3D U-net: learning dense volumetric
segmentation from sparse annotation, Proc. Int. Conf. Med. Image Comput. Computer-Assisted Intervention,
Athens, Greece, (2016) 424–432.
[33] M.I. Dale, A.M. Fischl, B. Sereno, Cortical surface-based analysis: I. segmentation and surface reconstruction,
Neuroimage 9(2) (1999) 195–207.
[34] E.C. Del Re, Y. Gao, R. Eckbo, T.L. Petryshen, G.A.M. Blokland, L.J. Seidman, J. Konishi, J.M. Goldstein, R.W.
McCarley, M.E. Shenton and S. Bouix, A new MRI masking technique based on multi-atlas brain segmentation in
controls and schizophrenia: A rapid and viable alternative to manual masking, J. Neuroimag. 26(1) (2016) 28–36.
[35] R. Dey and Y. Hong, Compnet: Complementary segmentation network for brain MRI extraction, Lect. Notes
Comput. Sci. 11072 (2018) 628–636.
[36] J. Dolz, C. Desrosiers and I. Ben Ayed, 3D fully convolutional networks for subcortical segmentation in MRI: A
large-scale study, Neuroimage 170 (2018) 456–470.
[37] J. Doshi, G. Erus, Y. Ou, B. Gaonkar and C. Davatzikos, Multi-atlas skull-stripping, Acad. Radiol. 20(12) (2013)
1566–1576.
[38] S.F. Eskildsen, P. Coup´e, V. Fonov, J.V. Manj´on, K.K. Leung, N. Guizard, S.N. Wassef, L.R. Østergaard, D.L.
Collins and Alzheimer’s Disease Neuroimaging Initiative, Beast: Brain extraction based on nonlocal segmentation
technique, Neuroimage 59(3) (2012) 2362–2373.
[39] M. Everingham, L. Van-Gool, C.K.I. Williams, J. Winn and A. Zisserman, The PASCAL visual object classes challenge 2012 (VOC2012) results, http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html,
(2012).
[40] A. Fedorov, J. Johnson, E. Damaraju, A. Ozerin, V. Calhoun and S. Plis, End-to-end learning of brain tissue
segmentation from imperfect labeling, Proc. Int. Jt. Conf. Neural Networks 2017 (2017) 3785–3792.
[41] C. Fennema-Notestine, I.B. Ozyurt, C.P. Clark, S. Morris, A. Bischoff-Grethe, M.W. Bondi, T.L. Jernigan, B.
Fischl, F. Segonne, D.W. Shattuck, R.M. Leahy, D.E. Rex, A.W. Toga, K.H. Zou and G.G. Brown, Quantitative
evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis,
bias correction, and slice location, Hum. Brain Mapp. 27(2) (2006) 99–113.
[42] B. Fischl, FreeSurfer, Neuroimage 62(2) (2012) 774–781.
[43] F.J. Galdames, F. Jaillet and C.A. Perez, An accurate skull stripping method based on simplex meshes and
histogram analysis for magnetic resonance images, J. Neurosci. Methods, 206(2) (2012) 103–119.
[44] O. Gambino, E. Daidone, M. Sciortino, R. Pirrone and E. Ardizzone, Automatic skull stripping in MRI based on
morphological filters and fuzzy C-means segmentation, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS
(2011) 5040–5043.
[45] Y. Gao, J. Li, H. Xu, M. Wang, C. Liu, Y. Cheng, M. Li, J. Yang and X. Li, A multi-view pyramid network for
skull stripping on neonatal T1-weighted MRI, Magn. Reson. Imag. 63 (2019) 70–79.
[46] L. Grady, Random walks for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 28(11) (2006) 1768–
1783.
[47] H.K. Hahn and H.O. Peitgen, The skull stripping problem in MRI solved by a single 3D watershed transform,
Lect. Notes Comput. Sci. 1935 (2000) 134–143.
[48] K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, Proc. IEEE Computer Soc.
Conf. Comput. Vision Pattern Recogn. 2016 (2016) 770–778.
[49] R.A. Heckemann, C. Ledig, K.R. Gray, P. Aljabar, D. Rueckert, J.V. Hajnal and A. Hammers, Brain extraction
using label propagation and group agreement: Pincram, PLoS One 10(7) (2015) 1–18.
[50] W.A. Hohne, K.H. Hanson, Interactive 3D segmentation of MRI and CT volumes using morphological operations,
J. Comput. Assist. Tomogr., 16(2) (1992) 285-294.
[51] A. Huang, R. Abugharbieh, R. Tam and A. Traboulsee, MRI brain extraction with combined expectation maximization and geodesic active contours, Sixth IEEE Int. Symp. Signal Process. Inf. Technol. ISSPIT, 107(1) (2006)
107–111.
[52] G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, Densely connected convolutional networks, Proc.
30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 (2017) 2261–2269.
[53] Y. Huo, Z. Xu, Y. Xiong, K. Aboud, P. Parvathaneni, S. Bao, C. Bermudez, S.M. Resnick, L.E. Cutting andB.A. Landman, 3D whole brain segmentation using spatially localized atlas network tiles, Neuroimage 194 (2019)
105–119.
[54] H. Hwang, H.Z.U. Rehman and S. Lee, 3D U-net for skull stripping in brain MRI, Appl. Sci. 9(3) (2019) 1–15.
[55] J.E. Iglesias, C.Y. Liu, P.M. Thompson and Z. Tu, Robust brain extraction across datasets and comparison with
publicly available methods, IEEE Trans. Med. Imag. 30(9) (2011)1617–1634.
[56] S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate
shift sergey, 32nd Int. Conf. Mach. Learn. ICML 2015(1) (2015) 448–456.
[57] F. Isensee, M. Schell, I. Pflueger, G. Brugnara, D. Bonekamp, U. Neuberger, A. Wick, H.-P. Schlemmer, S.
Heiland, W. Wick, M. Bendszus, K.H. Maier-Hein and P. Kickingereder, Automated brain extraction of multisequence MRI using artificial neural networks, Hum. Brain Mapp. 40(17) (2019) 4952–4964.
[58] C.R. Jack, M.A. Bernstein, N.C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P.J. Britson, J.L.
Whitwell, C. Ward, A.M. Dale, J.P. Felmlee, J.L. Gunter, D.L.G. Hill, R. Killiany, N. Schuff, S. Fox-Bosetti, C.
Lin, C. Studholme, C.S. DeCarli, G. Krueger, H.A. Ward, G.J. Metzger, K.T. Scott, R. Mallozzi, D. Blezek, J.
Levy, J.P. Debbins, A.S. Fleisher, M. Albert, R. Green, G. Bartzokis, G. Glover, J. Mugler and M.W. Weiner, The
alzheimer’s disease neuroimaging initiative (ADNI): MRI methods, J. Magn. Reson. Imag. 27(4) (2008) 685–691.
[59] M. Jenkinson, C.F. Beckmann, T.E.J. Behrens, M.W. Woolrich and S.M. Smith, FSL, Neuroimage 62(2) (2012)
782–90.
[60] R.K. Justice, E.M. Stokelyt, J.S. Strobel, R.E. Ideker and W.M. Smith, Medical image segmentation using 3-D
seeded region growing, Med. Imaging 1997 Image Process 3034(205) (1997) 900–910.
[61] J. Kleesiek, G. Urban, A. Hubert, D. Schwarz, K. Maier-Hein, M. Bendszus and A. Biller, Deep MRI brain
extraction: A 3D convolutional neural network for skull stripping, Neuroimage 129 (2016) 460–469.
[62] C. Ledig, R.A. Heckemann, A. Hammers, J.C. Lopez, V.F.J. Newcombe, A. Makropoulos, J. L¨otj¨onen, D.K.
Menon and D. Rueckert, Robust whole-brain segmentation: Application to traumatic brain injury, Med. Image
Anal. 21(1) (2015) 40–58.
[63] K.K. Leung, J. Barnes, M. Modat, G.R. Ridgway, J.W. Bartlett, N.C. Fox, S. Ourselin and Alzheimer’s Disease
Neuroimaging Initiative, Brain MAPS: An automated, accurate and robust brain extraction technique using a
template library, Neuroimage 55(3) (2011) 1091–1108.
[64] K.K. Leung, J. Barnes, G.R. Ridgway, J.W. Bartlett, M.J. Clarkson, K. Macdonald, N. Schuff, N.C. Fox, S.
Ourselin and Alzheimer’s Disease Neuroimaging Initiative, Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and alzheimer’s disease, Neuroimage 51(4) (2010) 1345–
1359.
[65] P. Li, Y. Zhao, Y. Liu, Q. Chen, F. Liu and C. Gao, Temporally consistent segmentation of brain tissue from
longitudinal MR data, IEEE Access 8 (2020) 3285–3293.
[66] G. Lin, A. Milan, C. Shen and I. Reid, RefineNet: Multi-path refinement networks for high-resolution semantic
segmentation, Cvpr (2017) 1925–1934.
[67] J.X. Liu, Y.S. Chen and L.F. Chen, Accurate and robust extraction of brain regions using a deformable model
based on radial basis functions, J. Neurosci. Methods, 183(2) (2009) 255–266.
[68] M. Liu, J. Zhang, E. Adeli and D. Shen, Landmark-based deep multi-instance learning for brain disease diagnosis,
Med. Image Anal. 43 (2018) 157–168.
[69] O. Lucena, R. Souza, L. Rittner, R. Frayne and R. Lotufo, Silver standard masks for data augmentation applied
to deep-learning-based skull-stripping, Proc. Int. Symp. Biomed. Imag. 2018 (2018) 1114–1117.
[70] O. Lucena, R. Souza, L. Rittner, R. Frayne and R. Lotufo, Convolutional neural networks for skull-stripping in
brain MR imaging using silver standard masks, Artif. Intell. Med. 98 (2019) 48–58.
[71] J.V. Manj´on, S.F. Eskildsen, P. Coup´e, J.E. Romero, D.L. Collins and M. Robles, Nonlocal intracranial cavity
extraction, Int. J. Biomed. Imag. (2014) 1–11.
[72] D.S. Marcus, A.F. Fotenos, J.G. Csernansky, J.C. Morris and R.L. Buckner, Open access series of imaging studies
(OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, J. Cogn.
Neurosci. 22(12) (2010) 2677–2684.
[73] J.C. Mazziotta, A.W. Toga, A. Evans, P. Fox and J. Lancaster, A probabilistic atlas of the human brain: theory
and rationale for its development, NeuroImage 2(2) (1995) 89–101.
[74] R. Mehta, A. Majumdar and J. Sivaswamy, Brainsegnet: A convolutional neural network architecture for automated segmentation of human brain structures, J. Med. Imag. 4(2) (2017) 024003.
[75] F. Milletari, N. Navab and S.A. Ahmadi, V-Net: Fully convolutional neural networks for volumetric medical
image segmentation, Proc. 4th Int. Conf. 3D Vision, 3DV 2016, (2016) 565–571.
[76] P. Moeskops, M.J.N.L. Benders, S.M. Chit¸, K.J. Kersbergen, F. Groenendaal, L.S. de Vries, M.A. Viergever
and I. Iˇsgum, Automatic segmentation of MR brain images of preterm infants using supervised classification,Neuroimage 118 (2015) 628–641.
[77] S.S. Mohseni Salehi, D. Erdogmus and A. Gholipour, Auto-context convolutional neural network (auto-net) for
brain extraction in magnetic resonance imaging, IEEE Trans. Med. Imag. 36(11) (2017) 2319–2330.
[78] J.C. Morris, The clinical dementia rating (CDR): Current version and scoring rules, Neurology 43(11) (1993)
2412–2414.
[79] S.G. Mueller, M.W. Weiner, L.J. Thal, R.C. Petersen, C. Jack, W. Jagust, J.Q. Trojanowski, A.W. Toga and L.
Beckett, The alzheimer’s disease neuroimaging initiative, Neuroimaging Clin. N. Am. 15(4) (2005) 869–877.
[80] N.E.L. Narv´aez and E.E.Z. Varela, A new approach on skull stripping of brain MRI based on saliency detection
using dictionary learning and sparse coding, Prospectiva 17(2) (2019).
[81] D.H.M. Nguyen, D.M. Nguyen, M.T.N. Truong, T. Nguyen, K.T. Tran, N.A. Triet, P.T. Bao and B.T. Nguyen,
ASMCNN: An efficient brain extraction using active shape model and convolutional neural networks, Inf. Sci. 591
(2022) 25–48.
[82] D. Nie, L. Wang, Y. Gao and D. Sken, Fully convolutional networks for multi-modality isointense infant brain
image segmentation, Proc. Int. Symp. Biomed. Imag. 2016 (2016) 1342–1345.
[83] NITRC, Robust brain extraction (ROBEX), J.E. ROBEX 1.2., https://www.nitrc.org/projects/robex, (2013).
[84] NITRC, Automatic registration toolbox (ART), http://www.nitrc.org/projects/art, (2020).
[85] J.G. Park and C. Lee, Skull stripping based on region growing for magnetic resonance brain images, Neuroimage,
47(4) (2009) 1394–1407.
[86] M. Rajchl, N. Pawlowski, D. Rueckert, P.M. Matthews and B. Glocker, NeuroNet: Fast and robust reproduction
of multiple brain image segmentation pipelines, arXiv preprint arXiv:1806.04224, (2018) 1–9.
[87] D.E. Rex, D.W. Shattuck, R.P. Woods, K.L. Narr, E. Luders, K. Rehm, S.E. Stoltzner, D.A. Rottenberg and
A.W. Toga, A meta-algorithm for brain extraction in MRI, Neuroimage 23(2) (2004) 625–637.
[88] T. Rohlfing, Image similarity and tissue overlaps as surrogates for image registration accuracy: Widely used but
unreliable, IEEE Trans. Med. Imag. 31(2) (2012) 153–163.
[89] O. Ronneberger, P. Fischer and T. Brox, U-net: Convolutional networks for biomedical image segmentation, Int.
Conf. Med. Image Comput. Computer-Assisted Interven. (2015) 234–241.
[90] E. Roura, A. Oliver, M. Cabezas, J.C. Vilanova, A. Rovira, L. Rami´o-Torrent`a and X. Llad´o, MARGA: Multispectral adaptive region growing algorithm for brain extraction on axial MRI, Comput. Methods Programs Biomed.
113(2) (2014) 655–673.
[91] S. Roy, J.A. Butman and D.L. Pham, Robust skull stripping using multiple MR image contrasts insensitive to
pathology, Neuroimage 146 (2017) 132–147.
[92] S. Roy and P. Maji, An accurate and robust skull stripping method for 3-D magnetic resonance brain images,
Magn. Reson. Imaging, 54 (2018) 46–57.
[93] E.H. Rubin, M. Storandt, J.P. Miller, D.A. Kinscherf, E.A. Grant, J.C. Morris and L. Berg, A prospective study
of cognitive function and onset of dementia in cognitively healthy elders, Arch. Neurol., 55(3) (1998) 395–401.
[94] S.A. Sadananthan, W. Zheng, M.W.L. Chee and V. Zagorodnov, Skull stripping using graph cuts, Neuroimage,
49(1) (2010) 225–239.
[95] M. Sato, S. Lakare, M. Wan and A. Kaufman, A gradient magnitude based region growing algorithm for accurate
segmentation, in Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101), (2000)
448–451.
[96] F. S´egonne, A.M. Dale, E. Busa, M. Glessner, D. Salat, H.K. Hahn and B. Fischl, A hybrid approach to the skull
stripping problem in MRI, Neuroimage 22(3) (2004) 1060–1075.
[97] A. Serag, M. Blesa, E.J. Moore, R. Pataky, S.A. Sparrow, A.G. Wilkinson, G. Macnaught, S.I. Semple and J.P.
Boardman, Accurate learning with few atlases (ALFA): An algorithm for MRI neonatal brain extraction and
comparison with 11 publicly available methods, Sci. Rep. 6 (2016) 23470.
[98] Z.Y. Shan, G.H. Yue and J.Z. Liu, Automated histogram-based brain segmentation in T1-weighted threedimensional magnetic resonance head images, Neuroimage 17(3) (2002) 1587–1598.
[99] D.W. Shattuck and R.M. Leahy, Brainsuite: An automated cortical surface identification tool, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 1935 (2000) 50–61.
[100] D.W. Shattuck, M. Mirza, V. Adisetiyo, C. Hojatkashani, G. Salamon, K.L. Narr, R.A. Poldrack, R.M. Bilder
and A.W. Toga, Construction of a 3D probabilistic atlas of human cortical structures, Neuroimage, 39(3) (2008)
1064–1080.
[101] D.W. Shattuck, S.R. Sandor-Leahy, K.A. Schaper, D.A. Rottenberg and R.M. Leahy, Magnetic resonance image
tissue classification using a partial volume model, Neuroimage 13(5) (2001) 856–876.
[102] E. Shelhamer, J. Long and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Trans.
Pattern Anal. Mach. Intell., 39(4) (2017) 640–651.[103] F. Shi, L. Wang, Y. Dai, J.H. Gilmore, W. Lin and D. Shen, LABEL: Pediatric brain extraction using learningbased meta-algorithm, Neuroimage 62(3) (2012) 1975–1986.
[104] S.M. Smith, Fast robust automated brain extraction, Hum. Brain Mapp. 17(3) (2002) 143–155.
[105] K. Somasundaram and K. Ezhilarasan, Automatic brain portion segmentation from magnetic resonance images
of head scans using gray scale transformation and morphological operations, J. Comput. Assist. Tomogr. 39(4)
(2015) 552–558.
[106] K. Somasundaram and T. Kalaiselvi, Fully automatic brain extraction algorithm for axial T2-weighted magnetic
resonance images, Comput. Biol. Med. 40(10) (2010) 811–822.
[107] K. Somasundaram and T. Kalaiselvi, Automatic brain extraction methods for T1 magnetic resonance images
using region labeling and morphological operations, Comput. Biol. Med. 41(8) (2011) 716–725.
[108] K. Somasundaram and R.S. Shankar, Skull stripping of MRI using clustering and 2D region growing method,
Image Process, NCIMP (2010) 133–135.
[109] W. Speier, J.E. Iglesias, L. El-Kara, Z. Tu and C. Arnold, Robust skull stripping of clinical glioblastoma multiforme data, Int. Conf. Med. Image Comput. Computer-Assisted Interven. 14(pt3) (2011) 659–666.
[110] K. Srinivasan and N.M. Nanditha, An intelligent skull stripping algorithm for MRI image sequences using
mathematical morphology, Biomed. Res. 29(16) (2018) 3201–3206.
[111] J.S. Suri, Two-dimensional fast magnetic resonance brain segmentation, IEEE Eng. Med. Biol. Mag. 20(4) (2001)
84–95.
[112] D.P. Waber, C.D. Moor, P.W. Forbes, C.R. Almli, K.N. Botteron, G. Leonard, D. Milovan, T. Paus, J. Rumsey
and Brain Development Cooperative Group, The NIH MRI study of normal brain development: Performance
of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery, J. Int.
Neuropsychol. Soc. 13(5) (2007) 729–746.
[113] L. Wang, Y. Chen, X. Pan, X. Hong and D. Xia, Level set segmentation of brain magnetic resonance images
based on local gaussian distribution fitting energy, J. Neurosci. Methods, 188(2) (2010) 316–325.
[114] Y. Wang, J. Nie, P.T. Yap, F. Shi, L. Guo and D. Shen, Robust deformable-surface-based skull-stripping for
large-scale studies yaping, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics) 6893(3) (2011) 635–642.
[115] S. Wang, Y. Shi, H. Zhuang, C. Qin and W. Li, Anatomical skull-stripping template and improved boundaryoriented quantitative segmentation evaluation metrics, J. Med Imaging Heal. Inf. 10 (2020) 693–704.
[116] J. Wang, Z. Sun, H. Ji, X. Zhang, T. Wang and Y. Shen, A fast 3D brain extraction and visualization framework
using active contour and modern OpenGL pipelines, IEEE Access 7 (2019) 156097–156109.
[117] B.D. Ward, Intracranial segmentation, Biophys. Res. Institute, Med. Coll. Wisconsin, (1999).
[118] B. Yilmaz, A. Durdu and G.D. Emlik, A new method for skull stripping in brain MRI using multistable cellular
neural networks, Neural Comput. Appl. 29(8) (2018) 79–95.
[119] L. Yu, J.Z. Cheng, Q. Dou, X. Yang, H. Chen, J. Qin and P.A. Heng, Automatic 3D cardiovascular MR
segmentation with densely-connected volumetric ConvNets, Lect. Notes Comput. Sci. (including Subser. Lect.
Notes Artif. Intell. Lect. Notes Bioinformatics), 10434 (2017) 287–295.
[120] W. Zhang R. Li, H. Deng, L. Wang, W. Lin, S. Ji and D. Shen, Deep convolutional neural networks for multimodality isointense infant brain image segmentation, Neuroimage 108 (2015) 214–224.
[121] Q. Zhang, L. Wang, X. Zong, W. Lin, G. Li and D. Shen, FRNET: Flattened residual network for infant MRI
skull stripping, Proc. Int. Symp. Biomed. Imag. 2019 (2019) 999–1002.
[122] A.H. Zhuang, D.J. Valentino and A.W. Toga, Skull-stripping magnetic resonance brain images using a modelbased level set, Neuroimage 32(1) (2006) 79–92.
Volume 13, Issue 1
March 2022
Pages 3783-3802
  • Receive Date: 10 November 2021
  • Revise Date: 10 January 2022
  • Accept Date: 01 February 2022