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State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms

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Abstract

Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping basically deals with the removal of the skull region for clinical analysis in brain segmentation tasks, and its accuracy and efficiency are quite crucial for diagnostic purposes. It requires more accurate and detailed methods for differentiating brain regions and the skull regions and is considered as a challenging task. This paper is focused on the transition of the conventional to the machine- and deep-learning-based automated skull stripping methods for brain MRI images. It is observed in this study that deep learning approaches have outperformed conventional and machine learning techniques in many ways, but they have their limitations. It also includes the comparative analysis of the current state-of-the-art skull stripping methods, a critical discussion of some challenges, model of quantifying parameters, and future work directions.

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Acknowledgments

This work is carried out at Medical Imaging and Diagnostics (MID) Lab at COMSATS University Islamabad, under the umbrella of National Center of Artificial Intelligence (NCAI), Pakistan.

Funding

This work has been supported by Higher Education Commission under Grant # 2(1064).

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Authors and Affiliations

  1. Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan

    Anam Fatima, Ahmad Raza Shahid, Basit Raza, Tahir Mustafa Madni & Uzair Iqbal Janjua

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  1. Anam Fatima
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  2. Ahmad Raza Shahid
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  3. Basit Raza
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Correspondence to Basit Raza.

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Fatima, A., Shahid, A.R., Raza, B. et al. State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms. J Digit Imaging 33, 1443–1464 (2020). https://doi.org/10.1007/s10278-020-00367-5

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