Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Applied Sciences

HafizZia Ur Rehman¹  Sungon Lee²  Hyunho Hwang² 

[1] Department of Mechatronics Engineering, Air University, Sector E-9, Islamabad 44000, Pakistan;School of Electrical Engineering, Hanyang University, Ansan 15588, Korea;

关键词: skull stripping;    brain segmentation;    brain extraction;    deep convolutional neural networks;    u-net;   

DOI  :  10.3390/app10051773

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown