MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation?

doi:10.1016/j.bspc.2023.104912

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation?
	Yang, Lei 1,3; Gu, Yuge 1,3; Bian, Guibin 1,2; Liu, Yanhong 1,3
刊名	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
	2023-08-01
卷号	85 页码:10
关键词	Semantic segmentation Deep learning Surgical instrument Multi-scale feature fusion Residual dense network
ISSN号	1746-8094
DOI	10.1016/j.bspc.2023.104912
通讯作者	Bian, Guibin(guibin.bian@ia.ac.cn) ; Liu, Yanhong(liuyh@zzu.edu.cn)
英文摘要	Accurate localization of surgical instruments is of utmost importance for precise robot-assisted surgeries. With the development of artificial intelligence, deep convolutional neural networks (DCNNs) have been widely employed for automatic image segmentation, owing to their strong ability to generate contextual features, especially in the encoder-decoder framework. However, existing segmentation networks lack the feature capturing capability on micro objects and have shortcomings in processing local semantic features. These limitations can affect the precise segmentation of surgical instruments. In response to these issues, this paper proposes a multi-scale attention fusion network called MAF-Net, which comprises residual dense module, a multi-scale atrous convolution (MSAC) module, and an attention fusion module (AFM). To improve the processing ability of local contextual features, we propose replacing skip connections with residual dense module to acquire stronger contexts. Furthermore, a MSAC module is proposed for local feature enhancement, thereby enhancing attention on multi-scale features. In addition, an AFM module is introduced to integrate multi-scale information by cross-scale feature fusion. Experimental results, using two public datasets, Endovis2017 and kvasir-instrument, demonstrate that the proposed network has the ability to achieve precise surgical instrument segmentation and outperforms related advanced methods.
资助项目	National Key Research & Development Project of China[2020YFB1313701] ; National Natural Science Foundation of China[62003309]
WOS关键词	IMAGE SEGMENTATION ; NEURAL-NETWORKS ; ROBOT
WOS研究方向	Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000982779200001
资助机构	National Key Research & Development Project of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53251]
专题	多模态人工智能系统全国重点实验室
通讯作者	Bian, Guibin; Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect & Informat Engn, Zhengzhou 450001, Henan, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Robot Percept & Control Engn Lab Henan Prov, Zhengzhou 450001, Henan, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Gu, Yuge,Bian, Guibin,et al. MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation?[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2023,85:10.
APA	Yang, Lei,Gu, Yuge,Bian, Guibin,&Liu, Yanhong.(2023).MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation?.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,85,10.
MLA	Yang, Lei,et al."MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation?".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 85(2023):10.