干旱区生态环境知识资源中心(Arid): MR brain segmentation based on DE-ResUnet combining texture features and background knowledge

Arid

DOI	10.1016/j.bspc.2022.103541
	MR brain segmentation based on DE-ResUnet combining texture features and background knowledge
	Wu, Liang; Hu, Shunbo; Liu, Changchun
通讯作者	Liu, CC
来源期刊	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN	1746-8094
EISSN	1746-8108
出版年	2022
卷号	75
英文摘要	The segmentation of the brain Magnetic Resonance (MR) images plays an essential role in neuroimaging research and clinical settings. Currently, deep learning combined with prior knowledge and attention mechanism is intensively implemented to solve the brain tissue segmentation task because of its superior performance. However, there are still two problems: firstly, some prior knowledge is difficult to obtain; secondly, incorrect attention is easy to produce in self-attention mechanism. To address these two issues, a novel dual encoder residual U-Net based on texture features and background knowledge, namely DE-ResUnet, is proposed in this work. In DE-ResUnet, the dual encoders for T1-weighted image and texture features are combined to learn hidden additional information. The introduction of channel attention mechanism (CAM) into two encoder and decoder paths facilitates the model to extract more useful informative features. Moreover, we design a strengthen module to refine the coarse segmentation, which can focus on brain tissue regions guided by background knowledge. We evaluate our proposed method on BrainWeb, OASIS-1 and CANDI datasets. The experimental results show that the proposed DE-ResUnet network achieves the accurate segmentation superior to that of several state-of-the-art methods. We also evaluate DE-ResUnet on the BraTS 2020 dataset and achieve good segmentation results. These experiments demonstrate that DE-ResUnet can not only segment normal brain MR images accurately, but also locate the area of the lesion in abnormal images. Our code is freely available at htt ps://github.com/LiangWUSDU/DE-ResUnet.
英文关键词	Brain MR image segmentation Background knowledge Texture feature Channel attention mechanism
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000783190300001
WOS关键词	NEURAL-NETWORKS ; IMAGE ; MODEL
WOS类目	Engineering, Biomedical
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391991
推荐引用方式 GB/T 7714	Wu, Liang,Hu, Shunbo,Liu, Changchun. MR brain segmentation based on DE-ResUnet combining texture features and background knowledge[J],2022,75.
APA	Wu, Liang,Hu, Shunbo,&Liu, Changchun.(2022).MR brain segmentation based on DE-ResUnet combining texture features and background knowledge.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,75.
MLA	Wu, Liang,et al."MR brain segmentation based on DE-ResUnet combining texture features and background knowledge".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 75(2022).