干旱区生态环境知识资源中心(Arid): A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

Arid

DOI	10.1109/ACCESS.2022.3175188
	A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation
	Dayananda, Chaitra; Choi, Jae Young; Lee, Bumshik
通讯作者	Lee, B
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2022
卷号	10 页码:52804-52817
英文摘要	This paper proposes an improved brain magnetic resonance imaging (MRI) segmentation model by integrating U-SegNet with fire modules and residual convolutions to segment brain tissues in MRI. In the proposed encoder-decoder method, the residual connections and squeeze-expand convolutional layers from the fire module lead to a lighter and more efficient architecture for brain MRI segmentation. The residual unit helps in the smooth training of the deep architecture, and features obtained from residual convolutions exhibit a superior representation of the features in the segmentation network. In addition, the method provides a design with more efficient architecture, fewer network parameters, and better segmentation accuracy for brain MRI. The proposed architecture was evaluated on publicly available open access series of imaging studies (OASIS) and internet brain segmentation repository (IBSR) datasets for brain tissue segmentation. The experimental results showed superior performance compared to other state-of-the-art methods on brain MRI segmentation with a dice similarity coefficient (DSC) score of 0.96 and Jaccard index (JI) of 0.92.
英文关键词	Image segmentation Magnetic resonance imaging Decoding Computer architecture Convolutional neural networks Convolution Computational modeling Brain tissue residual connection fire module magnetic resonance imaging
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000803533800001
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393067
推荐引用方式 GB/T 7714	Dayananda, Chaitra,Choi, Jae Young,Lee, Bumshik. A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation[J],2022,10:52804-52817.
APA	Dayananda, Chaitra,Choi, Jae Young,&Lee, Bumshik.(2022).A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation.IEEE ACCESS,10,52804-52817.
MLA	Dayananda, Chaitra,et al."A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation".IEEE ACCESS 10(2022):52804-52817.