干旱区生态环境知识资源中心(Arid): PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net

Arid

DOI	10.1016/j.bbe.2023.02.003
	PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention 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	2023
卷号	43 期号:2 页码:403-427
英文摘要	Background: The Corpus callosum (Cc) in the cerebral cortex is a bundle of neural fibers that facilitates inter-hemispheric communication. The Cc area and area of its sub-regions (also known as parcels) have been examined as a biomarker for cortical pathology and differen-tial diagnosis in neurodegenerative diseases such as Autism, Alzheimer's disease (AD), and more. Manual segmentation and parcellation of Cc are laborious and time-consuming. The present work proposes a novel work of automated parcellated Cc (PCc) segmentation that will serve as a potential biomarker to study and diagnose neurological disorders in brain MRI images.Method: In this perspective, the present work aims to develop an automated PCc segmen-tation from mid-sagittal T1-weighted (w) 2D brain MRI images using a deep learning-based fully convolutional network, a modified residual attention U-Net, referred to as PCcS-RAU-Net. The model has been modified to use a multi-class segmentation configuration with five target classes (parcels): rostrum, genu, mid-body, isthmus and splenium.Results: The experimental research uses two benchmark MRI datasets, ABIDE and OASIS. The proposed PCcS-RAU-Net outperformed existing methods on the ABIDE dataset with a DSC of 97.10% and MIoU of 94.43%. Furthermore, the model's performance is validated on the OASIS and Real clinical image (RCI) data and hence verifies the model's generaliza-tion capability.Conclusion: The proposed PCcS-RAU-Net model extracts essential characteristics such as the total area of the Cc (TCcA) to categorize MRI slices into healthy controls (HC) and dis-ease groups. Also, sub-regional areas, Cc1A to Cc5A, help study atrophy progression for early diagnosis.(c) 2023 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	Parcellated Corpus Callosum Parcels Segmentation Deep learning Multi-class Neurodegenerative diseases
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000955586000001
WOS关键词	ALZHEIMERS-DISEASE ; STRUCTURAL MRI ; OASIS
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/2XILL650/395542
推荐引用方式 GB/T 7714	Chandra, Anjali,Verma, Shrish,Raghuvanshi, A. S.,et al. PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net[J],2023,43(2):403-427.
APA	Chandra, Anjali,Verma, Shrish,Raghuvanshi, A. S.,&Bodhey, Narendra Kuber.(2023).PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net.BIOCYBERNETICS AND BIOMEDICAL ENGINEERING,43(2),403-427.
MLA	Chandra, Anjali,et al."PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net".BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 43.2(2023):403-427.