CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Adaptable Global Network for Whole-Brain Segmentation with Symmetry Consistency Loss
Zhao, Yuan-Xing1,2; Zhang, Yan-Ming2; Song, Ming2,3; Liu, Cheng-Lin1,2,4
Source PublicationCOGNITIVE COMPUTATION
ISSN1866-9956
2022-05-31
Pages14
Abstract

Segmenting the whole brain into a large number (for example, >= 100) of regions is challenging due to the complexity of the brain and the lack of annotated data. Deep neural network-based segmentation methods have shown promise, but due to the limitation of graphics processing unit (GPU) memory, they cannot fully exploit the brain structure information contained in 3D data. This paper proposes a memory-efficient framework to exploit the global brain structure for whole-brain segmentation. In this framework, upon extracting the brain region by using a skull-stripping subnetwork, a global modeling subnetwork is used to learn a global brain representation for segmentation, while an adaptable segmentation subnetwork is used to optimize the global representation during training and directly segment the whole brain during testing. This framework enables the representation to be learned from the global structure with reduced memory consumption, and segmentation is performed without splitting the brain into patches. To overcome the lack of annotated data, we also propose a semi-supervised method based on a symmetry consistency loss and a prior knowledge- based pseudolabel generation strategy. Extensive experiments on four datasets demonstrate that our method outperforms previously developed methods and achieves state-of-the-art performance. The method is computationally efficient in that segmenting a raw magnetic resonance imaging (MRI) image requires less than 2 s on a TITAN X GPU; our approach is much faster than multiatlas-based methods and previously proposed 3D deep learning methods. The code is publicly available at https://github.com/ZYX-MLer/AGNetwork.

KeywordWhole-brain segmentation Adaptable global network Semi-supervised learning Symmetry consistency loss
DOI10.1007/s12559-022-10011-9
WOS KeywordCONVOLUTIONAL NEURAL-NETWORKS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61773376] ; National Natural Science Foundation of China (NSFC)[61836014] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[31870984]
Funding OrganizationNational Key Research and Development Program ; National Natural Science Foundation of China (NSFC)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000803780800002
PublisherSPRINGER
Sub direction classification人工智能+医疗
planning direction of the national heavy laboratory其他
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49555
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorZhang, Yan-Ming
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100049, Peoples R China
4.Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Yuan-Xing,Zhang, Yan-Ming,Song, Ming,et al. Adaptable Global Network for Whole-Brain Segmentation with Symmetry Consistency Loss[J]. COGNITIVE COMPUTATION,2022:14.
APA Zhao, Yuan-Xing,Zhang, Yan-Ming,Song, Ming,&Liu, Cheng-Lin.(2022).Adaptable Global Network for Whole-Brain Segmentation with Symmetry Consistency Loss.COGNITIVE COMPUTATION,14.
MLA Zhao, Yuan-Xing,et al."Adaptable Global Network for Whole-Brain Segmentation with Symmetry Consistency Loss".COGNITIVE COMPUTATION (2022):14.
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