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3D U-Net for Skull Stripping in Brain MRI

Title
3D U-Net for Skull Stripping in Brain MRI
Author
이성온
Keywords
skull stripping; brian segmentation; brain extraction; deep convolutional neural networks; U-Net
Issue Date
2019-02
Publisher
MDPI
Citation
APPLIED SCIENCES-BASEL, v. 9, No. 3, Article no. 569
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.
URI
https://www.mdpi.com/2076-3417/9/3/569http://repository.hanyang.ac.kr/handle/20.500.11754/112400
ISSN
2076-3417
DOI
10.3390/app9030569
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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