Main Article Content
MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.
J. Bernsen. Dynamic thresholding of gray level images. In Proc. 8th Int. Conf. on Pattern Recognition ICPR, page 1251–1255, Paris, France, 27–31 Oct 1986.
A. S. Bhadauria, V. Bhateja, M. Nigam, and A. Arya. Skull stripping of brain mri using mathematical morphology. In S. Satapathy et al., editors, Smart Intelligent Computing and Applications, Proc. 3rd Int. Conf. Smart Computing and Informatics SCI 2018-19 (Vol. 1), volume 159 of Smart Innovation, Systems and Technologies, pages 775–780, Bhubaneswar, India, 21–22 Dec, 2018. 2020. https://doi.org/10.1007/978-981-13-9282-575. (Crossref)
P.-F. Chen, R. G. Steen, A. Yezzi, and H. Krim. Brain MRI t1-map and t1-weighted image segmentation in a variational framework. In Proc. 2009 IEEE Int. Conf. Acoustics, Speech and Signal Processing, pages 417–420, Taipei, Taiwan, 19-24 Apr 2009. https://doi.org/10.1109/ICASSP.2009.4959609. (Crossref)
M. Cheour. Advantages of brain MRI. Radiology Info. org, 2010.
C. A. Cocosco, V. Kollokian, R. K.-S. Kwan, and A. C. Evans. BrainWeb: Simulated Brain Database, 1998. https://brainweb.bic.mni.mcgill.ca. [Accessed 10 Oct 2020].
D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans. Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging, 17(3): 463–468, 1998. https://doi.org/10.1109/42.712135. (Crossref)
C. Dai, Y. Mo, E. Angelini, Y. Guo, and W. Bai. Transfer learning from partial annotations for whole brain segmentation. In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Proc. MICCAI Workshop on Domain Adaptation and Representation Transfer DART 2019, volume 11795 of Lecture Notes in Computer Science, pages 199–206, Shenzen, China, 13 Oct 2019. https://doi.org/10.1007/978-3-030-33391-123. (Crossref)
J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In Proc. 2015 IEEE Conf. Computer Vision and Pattern Recognition CVPR, pages 3992–4000, Boston, MA, USA, 7-12 Jun 2015. https://doi.org/10.1109/CVPR.2015.7299025. (Crossref)
A. V. Dalca, E. Yu, P. Golland, et al. Unsupervised deep learning for bayesian brain MRI segmentation. In D. Shen, T. Liu, T. M. Peters, et al., editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, volume 11766 of Lecture Notes in Computer Science, pages 356–365, 2019. https://doi.org/10.1007/978-3-030-32248-940. (Crossref)
R. Dey and Y. Hong. CompNet: Complementary segmentation network for brain MRI extraction. In Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention MICCAI 2018, volume 11072 of Lecture Notes in Computer Science, pages 628–636, Granada, Spain, 16-20 Sep 2018. https://doi.org/10.1007/978-3-030-00931-172. (Crossref)
J. Doshi, G. Erus, Y. Ou, et al. Multi-atlas skull-stripping. Academic Radiology, 20(12): 1566–1576,2013. https://doi.org/10.1016/j.acra.2013.09.010. (Crossref)
J. Dutta, D. Chakraborty, and D. Mondal. Multimodal segmentation of brain tumours in volumetric MRI scans of the brain using time-distributed u-net. In A. K. Das et al., editors, Proc. Conf. Computational Intelligence in Pattern Recognition CIPR 2019, volume 999 of Advances in Intelligent Systems and Computing, pages 715–725, 2020. https://doi.org/10.1007/978-981-13-9042-562. (Crossref)
Ø. A. Eide. Skull stripping MRI images of the brain using deep learning. Master’s thesis, Norwegian University of Science and Technology, 2018. https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2566509.
B. Erden, N. Gamboa, and S. Wood. 3D convolutional neural network for brain tumor segmentation. Technical report, Computer Science, Stanford University, Stanford, USA, 2017. http://cs231n.stanford.edu/reports/2017/pdfs/526.pdf.
M. Everingham, L. van Gool, C. Williams, et al. The PASCAL Visual Object Classes homepage, 2012. http://host.robots.ox.ac.uk/pascal/VOC/. [Accessed 10 Oct 2020].
A. Fatima, A. R. Shahid, B. Raza, et al. State-of-the-art traditional to the machine-and-deep-learning-based skull stripping techniques, models, and algorithms. Journal of Digital Imaging, 33(6): 1–22, 2020. https://doi.org/10.1007/s10278-020-00367-5. (Crossref)
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. Journal of Neuroscience Methods, 206(2): 103–119, 2012. https://doi.org/10.1016/j.jneumeth.2012.02.017. (Crossref)
K. He, G. Gkioxari, P. Dollár, and R. Girshick. Mask R-CNN. In Proc. IEEE Int. Conf. Computer Vision ICCV, pages 2961–2969, Venice, Italy, 22-29 Oct 2017. https://doi.org/10.1109/ICCV.2017.322. (Crossref)
R. Hu, P. Dollár, K. He, et al. Learning to segment every thing. In Proc. 2018 IEEE Conf. Computer Vision and Pattern Recognition CVPR, pages 4233–4241, Salt Lake City, USA, 18-23 Jun 2018. https://doi.org/10.1109/CVPR.2018.00445. (Crossref)
Y. Huang and L. C. Parra. Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed. PloS ONE, 10(5): e0125477, 2015. https://doi.org/10.1371/journal.pone.0125477. (Crossref)
H. Hwang, H. Z. U. Rehman, and S. Lee. 3D U-Net for skull stripping in brain MRI. Applied Sciences, 9(3): 569, 2019. https://doi.org/10.3390/app9030569. (Crossref)
F. Isensee, M. Schell, I. Pflueger, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping, 40(17): 4952–4964, 2019. https://doi.org/10.1002/hbm.24750. (Crossref)
T. Kim, K. Lee, S. Ham, et al. Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT. Scientific Reports, 10: 366, 2020. https://doi.org/10.1038/s41598-019-57242-9. (Crossref)
J. Kleesiek, G. Urban, A. Hubert, et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. NeuroImage, 129: 460–469, 2016. https://doi.org/10.1016/j.neuroimage.2016.01.024. (Crossref)
Z. Kuang, X. Deng, L. Yu, et al. Skull R-CNN: A CNN-based network for the skull fracture detection. In T. Arbel et al., editors, Proc. 3rd Conf. Medical Imaging with Deep Learning MIDL, volume 121 of Proceedings of Machine Learning Research, pages 382–392, Montreal, Canada, 06-08 Jul 2020. http://proceedings.mlr.press/v121/kuang20a.html.
K. Kushibar, S. Valverde, S. González-Villà, et al. Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Medical Image Analysis, 48: 177–186, 2018. https://doi.org/10.1016/j.media.2018.06.006. (Crossref)
P. J. LaMontagne, T. L. S. Benzinger, J. C. Morris, et al. Oasis-3: Longitudinal neuroimaging, clinical, and cognitive data set for normal aging and Alzheimer disease. medRxiv, 2019. https://doi.org/10.1101/2019.12.13.19014902. (Crossref)
P. J. LaMontagne, T. L. S. Benzinger, J. C. Morris, et al. OASIS Open Access Series of Imaging Studies, 2019. https://www.oasis-brains.org. [Accessed 10 Oct 2020].
K. Landheer, R. F. Schulte, M. S. Treacy, et al. Theoretical description of modern 1H in Vivo magnetic resonance spectroscopic pulse sequences. Journal of Magnetic Resonance Imaging, 51(4): 1008–1029, 2020. https://doi.org/10.1002/jmri.26846. (Crossref)
Y. Li, H. Li, and Y. Fan. ACEnet: Anatomical context-encoding network for neuroanatomy segmentation. arXiv, 2020. arXiv: 2002.05773 [eess. IV]. https://arxiv.org/abs/2002.05773.
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2): 318–327, 2020. https://doi.org/10.1109/TPAMI.2018.2858826. (Crossref)
T.-Y. Lin, P. Dollár, R. Girshick, et al. Feature pyramid networks for object detection. In Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition CVPR, pages 936–944, Honolulu, Hawaii, 22-25 Jul 2017. https://doi.org/10.1109/CVPR.2017.106. (Crossref)
T.-Y. Lin, G. Patterson, M. R. Ronchi, et al. COCO. Common Objects in Context, 2020. https://cocodataset.org. [Accessed 10 Oct 2020].
X.-B. Lin, X.-X. Li, and D.-M. Guo. Registration error and intensity similarity based label fusion for segmentation. IRBM, 40(2): 78–85, 2019. https://doi.org/10.1016/j.irbm.2019.02.001. (Crossref)
Y. Liu, Y. Wei, and C. Wang. Subcortical brain segmentation based on atlas registration and linearized kernel sparse representative classifier. IEEE Access, 7: 31547–31557, 2019. https://doi.org/10.1109/ACCESS.2019.2902463. (Crossref)
O. Lucena, R. Souza, L. Rittner, et al. Silver standard masks for data augmentation applied to deep-learning-based skull-stripping. In Proc. 2018 IEEE 15th International Symposium on Biomedical Imaging ISBI, pages 1114–1117, Washington, USA, 4-7 Apr 2018. https://doi.org/10.1109/ISBI.2018.8363766. (Crossref)
O. Lucena, R. Souza, L. Rittner, et al. Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks. Artificial Intelligence in Medicine, 98: 48–58, 2019. https://doi.org/10.1016/j.artmed.2019.06.008. (Crossref)
J. V. Manjón, J. E. Romero, R. Vivo-Hernando, et al. Deep ICE: A deep learning approach for MRI intracranial cavity extraction. arXiv, 2020. arXiv: 2001.05720 [q-bio.QM]. https://arxiv.org/abs/2001.05720.
R. Mehta, A. Majumdar, and J. Sivaswamy. BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures. Journal of Medical Imaging, 4(2): 1–11, 2017. https://doi.org/10.1117/1.JMI.4.2.024003. (Crossref)
S. Moldovanu, L. Moraru, and A. Biswas. Robust skull-stripping segmentation based on irrational mask for magnetic resonance brain images. Journal of Digital Imaging, 28(6): 738–747, 2015. https://doi.org/10.1007/s10278-015-9776-6. (Crossref)
W. Niblack. An Introduction to Digital Image Processing. Prentice Hall, 1986.
M. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics, 9(1): 62–66, 1979. https://doi.org/10.1109/TSMC.1979.4310076. (Crossref)
G. Prasad, A. A. Joshi, A. Feng, et al. Skull-stripping with machine learning deformable organisms. Journal of Neuroscience Methods, 236: 114–124, 2014. https://doi.org/10.1016/j.jneumeth.2014.07.023. (Crossref)
H. Z. U. Rehman, H. Hwang, and S. Lee. Conventional and deep learning methods for skull stripping in brain MRI. Applied Sciences, 10(5): 1773, 2020. https://doi.org/10.3390/app10051773. (Crossref)
S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: towards real-time object detection with region proposal networks. arXiv, 2015. arXiv: 1506.01497 [cs.CV]. http://arxiv.org/abs/1506.01497.
S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137–1149, 2017. https://doi.org/10.1109/TPAMI.2016.2577031. (Crossref)
S. Roy, A. Knutsen, A. Korotcov, et al. A deep learning framework for brain extraction in humans and animals with traumatic brain injury. In Proc. 2018 IEEE 15th International Symposium on Biomedical Imaging ISBI, pages 687–691, Washington, USA, 4-7 Apr 2018. https://doi.org/10.1109/ISBI.2018.8363667. (Crossref)
S. Roy and P. Maji. A simple skull stripping algorithm for brain MRI. In Proc. 2015 8th Int. Conf. Advances in Pattern Recognition ICAPR, pages 1–6, Kolkata, India, 4-7 Jan 2015. https://doi.org/10.1109/ICAPR.2015.7050671. (Crossref)
S. Roy and P. Maji. An accurate and robust skull stripping method for 3-D magnetic resonance brain images. Magnetic Resonance Imaging, 54: 46–57, 2018. https://doi.org/10.1016/j.mri.2018.07.014. (Crossref)
G. Ruffini, M. D. Fox, O. Ripolles, et al. Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields. Neuroimage, 89: 216–225, 2014. https://doi.org/10.1016/j.neuroimage.2013.12.002. (Crossref)
J. Sauvola and M. Pietikäinen. Adaptive document image binarization. Pattern Recognition, 33(2): 225–236, 2000. https://doi.org/10.1016/S0031-3203(99)00055-2. (Crossref)
D. Selvathi and T. Vanmathi. Brain region segmentation using convolutional neural network. In 2018 4th Int. Conf. Electrical Energy Systems ICEES, pages 661–666, Chennai, India, 7-9 Feb 2018. https://doi.org/10.1109/ICEES.2018.8442394. (Crossref)
H. Tariq, A. Muqeet, A. Burney, Akhtar H. M., and H. Azam. Otsu’s segmentation: Review, visualization and analysis in context of axial brain MR slices. Journal of Theoretical & Applied Information Technology, 95(22), 2017. http://www.jatit.org/volumes/Vol95No22/9Vol95No22.pdf.
H. Tariq and M. Shahbaz. MAFA: Multispectral adaptive fuzzy algorithm for edge detection on MRI of head scan. International Journal of Computer Applications, 182(48): 49–54, 2019. https://doi.org/10.5120/IJCA2019918737. (Crossref)
G. Valvano, N. Martini, A. Leo, et al. Training of a skull-stripping neural network with efficient data augmentation. arXiv, 2018. arXiv: 1810.10853 [cs.CV].https://arxiv.org/abs/1810.10853.
A. van der Plas. MRI techniques, 2016. https://www.startradiology.com/the-basics/mri-technique/. [Accessed 10 Oct 2020].
M. Wang and P. Li. Label fusion method combining pixel greyscale probability for brain MR segmentation. Scientific Reports, 9: 17987, 2019. https://doi.org/10.1038/s41598-019-54527-x. (Crossref)
X. Wang, R. Girshick, A. Gupta, and K. He. Non-local neural networks. In Proc. 2018 IEEE Conf. Computer Vision and Pattern Recognition CVPR, pages 7794–7803, Salt Lake City, USA, 18-23 Jun 2018. https://doi.org/10.1109/CVPR.2018.00813. (Crossref)
A. Worth, C. Haselgrove, and D. Kennedy. IBSR. The Internet Brain Segmentation Repository, 2007. https://www.nitrc.org/projects/ibsr/. [Accessed 10 Oct 2020].
L. Xu, H. Liu, E. Song, et al. Automatic labeling of MR brain images through extensible learning and atlas forests. Medical Physics, 44(12): 6329–6340, 2017. https://doi.org/10.1002/mp.12591. (Crossref)
B. Yilmaz, A. Durdu, and G. D. Emlik. A new method for skull stripping in brain MRI using multistable cellular neural networks. Neural Computing and Applications, 29(8): 79–95, 2018. https://doi.org/10.1007/s00521-016-2834-2. (Crossref)
J. Zhou, H.-Y. Heo, L. Knutsson, et al. APT-weighted MRI: Techniques, current neuro applications, and challenging issues. Journal of Magnetic Resonance Imaging, 50(2): 347–364, 2019. https://doi.org/10.1002/jmri.26645. (Crossref)