CeLNet: a correlation-enhanced lightweight network for medical image segmentation

doi:10.1088/1361-6560/acd519

	CeLNet: a correlation-enhanced lightweight network for medical image segmentation
	Zhang, Bangze 1; Wang, Xiaoyan 1; Liu, Lianggui 2; Zhang, Denghui 2; Huang, Xiaojie 3; Xia, Ming 1; Jiang, Weiwei 1; Huang, Xiangsheng4
发表期刊	PHYSICS IN MEDICINE AND BIOLOGY
ISSN	0031-9155
	2023-06-07
卷号	68 期号:11 页码:14
通讯作者	Wang, Xiaoyan(xiaoyanwang@zjut.edu.cn)
摘要	Objective. Convolutional neural networks have been widely adopted for medical image segmentation with their outstanding feature representation capabilities. As the segmentation accuracy gets constantly updated, the complexity of networks increases as well. Complex networks can achieve better performance but require more parameters and are hard to train with limited resources, while lightweight models are faster but cannot fully utilize the contextual information of medical images. In this paper, we focus on better balancing the efficiency and accuracy. Approach. We propose a correlation-enhanced lightweight network (CeLNet) for medical image segmentation, which adopts a siamese structure for weight sharing and parameter saving. Through the feature reuse and feature stacking of parallel branches, a point-depth convolution parallel block (PDP Block) is proposed to reduce the model parameters and computational cost while improving the feature extraction capability of encoder. A relation module is also designed to extract feature correlations of input slices, which utilizes global and local attention to enhance feature connections, while reducing feature differences through element subtraction, and finally obtains contextual information of associated slices to improve the segmentation performance. Main results. We conduct extensive experiments on the LiTS2017, MM-WHS and ISIC2018 datasets, and the proposed model consumes merely 5.18M parameters but achieves excellent segmentation performance, specifically, a DSC of 0.9233 in LiTS2017 dataset, an average DSC of 0.7895 on MM-WHS dataset and an average DSC of 0.8401 on ISIC2018 dataset. Significance. CeLNet achieves state-of-the-art performance in multiple datasets while ensuring lightweight.
关键词	medical image multi-slice segmentation lightweight contextual learning
DOI	10.1088/1361-6560/acd519
收录类别	SCI
语种	英语
资助项目	Zhejiang Provincial Natural Science Foundation of China[LY23F030007] ; Zhejiang Provincial Natural Science Foundation of China[LQ20H160052] ; Zhejiang Provincial Natural Science Foundation of China[LY20H180006] ; National Natural Science Foundation of China[62273308] ; National Natural Science Foundation of China[61701442] ; Zhejiang Provincial Research Project on the Application of Public Welfare Technologies[LGF22F020023] ; National Key R&D Program of China[2020YFC2006406]
项目资助者	Zhejiang Provincial Natural Science Foundation of China ; National Natural Science Foundation of China ; Zhejiang Provincial Research Project on the Application of Public Welfare Technologies ; National Key R&D Program of China
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000997151200001
出版者	IOP Publishing Ltd
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53400
专题	复杂系统认知与决策实验室_决策指挥与体系智能
通讯作者	Wang, Xiaoyan
作者单位	1.Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China 2.Zhejiang Shuren Univ, Coll Informat & Technol, Hangzhou 310015, Peoples R China 3.Zhejiang Univ, Affiliated Hosp 2, Sch Med, Hangzhou 310009, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Bangze,Wang, Xiaoyan,Liu, Lianggui,et al. CeLNet: a correlation-enhanced lightweight network for medical image segmentation[J]. PHYSICS IN MEDICINE AND BIOLOGY,2023,68(11):14.
APA	Zhang, Bangze.,Wang, Xiaoyan.,Liu, Lianggui.,Zhang, Denghui.,Huang, Xiaojie.,...&Huang, Xiangsheng.(2023).CeLNet: a correlation-enhanced lightweight network for medical image segmentation.PHYSICS IN MEDICINE AND BIOLOGY,68(11),14.
MLA	Zhang, Bangze,et al."CeLNet: a correlation-enhanced lightweight network for medical image segmentation".PHYSICS IN MEDICINE AND BIOLOGY 68.11(2023):14.