干旱区生态环境知识资源中心(Arid): MAS-CL: An End-to-End Multi-Atlas Supervised Contrastive Learning Framework for Brain ROI Segmentation

Arid

DOI	10.1109/TIP.2024.3431451
	MAS-CL: An End-to-End Multi-Atlas Supervised Contrastive Learning Framework for Brain ROI Segmentation
	Sun, Liang; Fu, Yanling; Zhao, Junyong; Shao, Wei; Zhu, Qi; Zhang, Daoqiang
通讯作者	Zhang, DQ
来源期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
EISSN	1941-0042
出版年	2024
卷号	33 页码:4319-4333
英文摘要	Brain region-of-interest (ROI) segmentation with magnetic resonance (MR) images is a basic prerequisite step for brain analysis. The main problem with using deep learning for brain ROI segmentation is the lack of sufficient annotated data. To address this issue, in this paper, we propose a simple multi-atlas supervised contrastive learning framework (MAS-CL) for brain ROI segmentation with MR images in an end-to-end manner. Specifically, our MAS-CL framework mainly consists of two steps, including 1) a multi-atlas supervised contrastive learning method to learn the latent representation using a limited amount of voxel-level labeling brain MR images, and 2) brain ROI segmentation based on the pre-trained backbone using our MSA-CL method. Specifically, different from traditional contrastive learning, in our proposed method, we use multi-atlas supervised information to pre-train the backbone for learning the latent representation of input MR image, i.e., the correlation of each sample pair is defined by using the label maps of input MR image and atlas images. Then, we extend the pre-trained backbone to segment brain ROI with MR images. We perform our proposed MAS-CL framework with five segmentation methods on LONI-LPBA40, IXI, OASIS, ADNI, and CC359 datasets for brain ROI segmentation with MR images. Various experimental results suggested that our proposed MAS-CL framework can significantly improve the segmentation performance on these five datasets.
英文关键词	Contrastive learning multi-atlas brain segmentation
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001282380500004
WOS关键词	3D PROBABILISTIC ATLAS ; MR IMAGE ; NETWORK ; SERIES
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404151
推荐引用方式 GB/T 7714	Sun, Liang,Fu, Yanling,Zhao, Junyong,et al. MAS-CL: An End-to-End Multi-Atlas Supervised Contrastive Learning Framework for Brain ROI Segmentation[J],2024,33:4319-4333.
APA	Sun, Liang,Fu, Yanling,Zhao, Junyong,Shao, Wei,Zhu, Qi,&Zhang, Daoqiang.(2024).MAS-CL: An End-to-End Multi-Atlas Supervised Contrastive Learning Framework for Brain ROI Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,4319-4333.
MLA	Sun, Liang,et al."MAS-CL: An End-to-End Multi-Atlas Supervised Contrastive Learning Framework for Brain ROI Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):4319-4333.