• Ostrom, Q. T. et al. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015. Neuro Oncol. 20(4), 1–86 (2018).

    Article 

    Google Scholar 

  • Pei, L., Vidyaratne, L., Rahman, M. M. & Iftekharuddin, K. M. Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images. Sci. Rep. 10(1), 1–11 (2020).

    Article 

    Google Scholar 

  • Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv:1811.02629 (2018).

  • Pereira, S., Meier, R., Alves, V., Reyes, M. & Silva, C. A. Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications 106–114 (Springer, 2018).

    Chapter 

    Google Scholar 

  • Havaei, M. et al. Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017).

    PubMed 
    Article 

    Google Scholar 

  • Myronenko, A. 3D MRI brain tumor segmentation using autoencoder regularization. In International MICCAI Brainlesion Workshop 311–320 (Springer, 2018).

    Google Scholar 

  • Pei, L., Reza, S. M., Li, W., Davatzikos, C. & Iftekharuddin, K. M. (2017) Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. in Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134. (International Society for Optics and Photonics, 2017).

  • Reza, S. M., Mays, R. & Iftekharuddin, K. M. Multi-fractal detrended texture feature for brain tumor classification. in Medical Imaging 2015: Computer-Aided Diagnosis, vol. 9414 (International Society for Optics and Photonics, 2015).

  • Kalavathi, P. & Prasath, V. S. Methods on skull stripping of MRI head scan images: A review. J. Dig. Imaging 29(3), 365–379 (2016).

    CAS 
    Article 

    Google Scholar 

  • Kalkers, N. F. et al. Longitudinal brain volume measurement in multiple sclerosis: Rate of brain atrophy is independent of the disease subtype. Arch. Neurol. 59(10), 1572–1576 (2002).

    PubMed 
    Article 

    Google Scholar 

  • De Boer, R. et al. White matter lesion extension to automatic brain tissue segmentation on MRI. Neuroimage 45(4), 1151–1161 (2009).

    PubMed 
    Article 

    Google Scholar 

  • Tanskanen, P. et al. Hippocampus and amygdala volumes in schizophrenia and other psychoses in the Northern Finland 1966 birth cohort. Schizophr. Res. 75(2–3), 283–294 (2005).

    PubMed 
    Article 

    Google Scholar 

  • Rusinek, H. et al. Alzheimer disease: Measuring loss of cerebral gray matter with MR imaging. Radiology 178(1), 109–114 (1991).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Bakas, S. et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286, 1–10 (2017).

    Google Scholar 

  • Kleesiek, J. et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. Neuroimage 129, 460–469 (2016).

    PubMed 
    Article 

    Google Scholar 

  • Fennema-Notestine, C. et al. 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), 99–113 (2006).

    PubMed 
    Article 

    Google Scholar 

  • Brummer, M. E., Mersereau, R. M., Eisner, R. L. & Lewine, R. R. Automatic detection of brain contours in MRI data sets. IEEE Trans. Med. Imaging 12(2), 153–166 (1993).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A. & Leahy, R. M. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13(5), 856–876. https://doi.org/10.1006/nimg.2000.0730 (2001).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Hahn, H. K. & Peitgen, H.-O. The skull stripping problem in MRI solved by a single 3D watershed transform. In International Conference on Medical Image Computing and Computer-Assisted Intervention 134–143 (Springer, 2000).

    Google Scholar 

  • Grau, V., Mewes, A., Alcaniz, M., Kikinis, R. & Warfield, S. K. Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med. Imaging 23(4), 447–458 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26(3), 839–851 (2005).

    PubMed 
    Article 

    Google Scholar 

  • Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Jenkinson, M., Pechaud, M. & Smith, S. BET2: MR-based estimation of brain, skull and scalp surfaces. in Eleventh annual meeting of the organization for human brain mapping, vol. 17, 167 (2005).

  • Liu, J.-X., Chen, Y.-S. & Chen, L.-F. Accurate and robust extraction of brain regions using a deformable model based on radial basis functions. J. Neurosci. Methods 183(2), 255–266 (2009).

    MathSciNet 
    PubMed 
    Article 

    Google Scholar 

  • Aboutanos, G. B., Nikanne, J., Watkins, N. & Dawan, B. Model creation and deformation for the automatic segmentation of the brain in MR images. IEEE Trans. Biomed. Eng. 46(11), 1346–1356 (1999).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Leung, K. K. et al. Brain MAPS: An automated, accurate and robust brain extraction technique using a template library. Neuroimage 55(3), 1091–1108 (2011).

    PubMed 
    Article 

    Google Scholar 

  • Eskildsen, S. F. et al. BEaST: Brain extraction based on nonlocal segmentation technique. Neuroimage 59(3), 2362–2373 (2012).

    PubMed 
    Article 

    Google Scholar 

  • Roy, S., Butman, J. A., Pham, D. L. & Initiative, A. D. N. Robust skull stripping using multiple MR image contrasts insensitive to pathology. Neuroimage 146, 132–147 (2017).

    PubMed 
    Article 

    Google Scholar 

  • Goodfellow, I., Bengio, Y., Courville, A. & Bengio, Y. Deep Learning (No. 2) (MIT Press, 2016).

    MATH 

    Google Scholar 

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521(7553), 436–444 (2015).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Young, T., Hazarika, D., Poria, S. & Cambria, E. Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018).

    Article 

    Google Scholar 

  • Voulodimos, A., Doulamis, N., Doulamis, A. & Protopapadakis, E. Deep learning for computer vision: A brief review. Comput. Intell. Neurosci. 2018, 1–10 (2018).

    Google Scholar 

  • Hannun, A. et al. Deep speech: Scaling up end-to-end speech recognition. arXiv:1412.5567 (2014).

  • Chen, H., Dou, Q., Yu, L., Qin, J. & Heng, P.-A. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage 170, 446–455 (2018).

    PubMed 
    Article 

    Google Scholar 

  • Pei, L., Vidyaratne, L., Hsu, W.-W., Rahman, M. M. & Iftekharuddin, K. M. Brain Tumor Classification Using 3D Convolutional Neural Network 335–342 (Springer, 2020).

    Google Scholar 

  • Gordienko, Y. et al. Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. In International Conference on Computer Science, Engineering and Education Applications 638–647 (Springer, 2018).

    Google Scholar 

  • Hwang, H., Rehman, H. Z. U. & Lee, S. 3D U-net for skull stripping in brain MRI. Appl. Sci. 9(3), 569 (2019).

    Article 

    Google Scholar 

  • Thakur, S. et al. Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage 220, 117081 (2020).

    PubMed 
    Article 

    Google Scholar 

  • Thakur, S. P. et al. Skull-stripping of glioblastoma MRI scans using 3D deep learning. In International MICCAI Brainlesion Workshop 57–68 (Springer, 2019).

    Google Scholar 

  • Rohlfing, T., Zahr, N. M., Sullivan, E. V. & Pfefferbaum, A. The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819. https://doi.org/10.1002/hbm.20906 (2010).

    PubMed 
    Article 

    Google Scholar 

  • Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 234–241 (Springer, 2015).

    Google Scholar 

  • Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014).

  • Liu, M., Chen, L., Du, X., Jin, L. & Shang, M. Activated gradients for deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 44, 1–13 (2021).

    Google Scholar 

  • Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945).

    Article 

    Google Scholar 

  • Huttenlocher, D. P., Klanderman, G. A. & Rucklidge, W. J. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993).

    Article 

    Google Scholar 

  • de Boer, R. et al. Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods. Neuroimage 51(3), 1047–1056 (2010).

    PubMed 
    Article 

    Google Scholar 

  • Iglesias, J. E., Liu, C.-Y., Thompson, P. M. & Tu, Z. Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans. Med. Imaging 30(9), 1617–1634 (2011).

    PubMed 
    Article 

    Google Scholar 

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