Abstract
Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.
Highlights
Among the various medical imaging techniques, magnetic resonance imaging (MRI) of the brain is one of the most prevalent image acquisitions performed in the diagnostic centers and hospitals.The acquisition of a brain MRI scan is noninvasive and nondestructive
Clinicians and practitioners still depend on manual skull stripping due to the communication gap and lack of interaction between the researchers and practitioners
The objective of several skull stripping tools is to do only the research, and they are barely useful for clinicians
Summary
Among the various medical imaging techniques, magnetic resonance imaging (MRI) of the brain is one of the most prevalent image acquisitions performed in the diagnostic centers and hospitals. The whole brain skull stripping is a laborious task due to low contrast images, obscure boundaries of the extraction becomes more challenging and limited in the presence of varying acquisition parameters or brain in the MRI, and the absence of intensity standardization [22]. For skull stripping in brain MRIs, manual brain and non-brain segmentation methods are considered more robust and accurate than semi or fully automatic methods. Generally,on manual delineation of the treated as the stripping is to draw the boundaries brain MRIs and isolate thebrain braintissue tissue is from the skull Is commonly utilized to to in MRI volume is treated as the “ground truth” or “gold standard,” and is commonly utilized validate other semi-automatic and automatic skull stripping methods.
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