干旱区生态环境知识资源中心(Arid): CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net

Arid

DOI	10.1016/j.bbe.2021.12.008
	CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net
	Chandra, Anjali; Verma, Shrish; Raghuvanshi, A. S.; Bodhey, Narendra Kuber
通讯作者	Chandra, A
来源期刊	BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
ISSN	0208-5216
出版年	2022
卷号	42 期号:1 页码:187-203
英文摘要	Background: Corpus Callosum (CC) is the most prominent white matter bundle in the human brain that connects the left and right cerebral hemispheres. The present paper proposes a novel method for CC segmentation from 2D T1-weighted mid-sagittal brain MRI. The robust segmentation of CC in the mid-sagittal plane plays a vital role in the quantitative study of CC structural features related to various neurological disorders such as Autism, epilepsy, Alzheimer's disease, and more. Methodology: In this perspective, the current work proposes a Fully Convolutional Network (FCN), a deep learning architecture-based U-Net model for automated CC segmentation from 2D brain MRI images referred to as CCsNeT. The architecture consists of a 35-layers deep, fully convolutional network with two paths, namely contracting and extracting, connected in a U-shape that automatically extracts spatial information. Results: This attempt uses the benchmark brain MRI database comprising ABIDE and OASIS for the experimental investigation. Compared to existing CC segmentation methodologies, the proposed CCsNeT presented improved results achieving Dice Coefficient = 96.74%, and Sensitivity = 97.01% with ABIDE dataset and were further validated against the variants of U-Net model U-Net++, MultiResU-Net, and CE-Net. Further, the performance of CCsNeT has been validated on OASIS and Real-Time Images dataset. Conclusion: Finally, the proposed CCsNeT extracts important CC characteristics such as CC area (CCA) and total brain area (TBA) to categorize the considered 2D MRI slices into control and autism spectrum disorder (ASD) groups, thereby minimizing the inter-observer and intra-observer variability. (c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
英文关键词	Corpus Callosum Segmentation Deep learning Fully Convolutional Network U-Net
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000784316500011
WOS关键词	MRI
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/2XILL650/391970
推荐引用方式 GB/T 7714	Chandra, Anjali,Verma, Shrish,Raghuvanshi, A. S.,et al. CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net[J],2022,42(1):187-203.
APA	Chandra, Anjali,Verma, Shrish,Raghuvanshi, A. S.,&Bodhey, Narendra Kuber.(2022).CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net.BIOCYBERNETICS AND BIOMEDICAL ENGINEERING,42(1),187-203.
MLA	Chandra, Anjali,et al."CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net".BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 42.1(2022):187-203.