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Appl. Sci. 2019, 9(3), 569; https://doi.org/10.3390/app9030569

3D U-Net for Skull Stripping in Brain MRI

1
Department of Electrical and Electronics Engineering, Hanyang University, Ansan 15588, Korea
2
Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Korea
3
School of Electrical Engineering, Hanyang University, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Received: 11 December 2018 / Revised: 1 February 2019 / Accepted: 2 February 2019 / Published: 8 February 2019
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
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Abstract

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953. View Full-Text
Keywords: skull stripping; brian segmentation; brain extraction; deep convolutional neural networks; U-Net skull stripping; brian segmentation; brain extraction; deep convolutional neural networks; U-Net
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hwang, H.; Rehman, H.Z.U.; Lee, S. 3D U-Net for Skull Stripping in Brain MRI. Appl. Sci. 2019, 9, 569.

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