LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images

doi:10.1088/1361-6560/ad2011

	LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images
	Yang, Chaozhi 1; Li, Bei 2; Xiao, Qian 1; Bai, Yun 1; Li, Yachuan 1; Li, Zongmin 1; Li, Hongyi 2; Li, Hua 3
	2024-02-21
发表期刊	PHYSICS IN MEDICINE AND BIOLOGY
ISSN	0031-9155
卷号	69 期号:4 页码:15
摘要	Objective. Retinal vessel segmentation from optical coherence tomography angiography (OCTA) volumes is significant in analyzing blood supply structures and the diagnosing ophthalmic diseases. However, accurate retinal vessel segmentation in 3D OCTA remains challenging due to the interference of choroidal blood flow signals and the variations in retinal vessel structure. Approach. This paper proposes a layer attention network (LA-Net) for 3D-to-2D retinal vessel segmentation. The network comprises a 3D projection path and a 2D segmentation path. The key component in the 3D path is the proposed multi-scale layer attention module, which effectively learns the layer features of OCT and OCTA to attend to the retinal vessel layer while suppressing the choroidal vessel layer. This module also efficiently captures 3D multi-scale information for improved semantic understanding during projection. In the 2D path, a reverse boundary attention module is introduced to explore and preserve boundary and shape features of retinal vessels by focusing on non-salient regions in deep features. Main results. Experimental results in two subsets of the OCTA-500 dataset showed that our method achieves advanced segmentation performance with Dice similarity coefficients of 93.04% and 89.74%, respectively. Significance. The proposed network provides reliable 3D-to-2D segmentation of retinal vessels, with potential for application in various segmentation tasks that involve projecting the input image. Implementation code: https://github.com/y8421036/LA-Net.
关键词	retinal vessel segmentation 3D-to-2D multi-scale layer attention reverse boundary attention OCTA volume
DOI	10.1088/1361-6560/ad2011
收录类别	SCI
语种	英语
资助项目	General Research Projects of Beijing Educations Committee in China ; National Key R&D Program of China[2019YFF0301800] ; National Natural Science Foundation of China[61379106] ; National Natural Science Foundation of China[61806199] ; Shandong Provincial Natural Science Foundation[ZR2015FM011] ; [KM201910005013]
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:001159796700001
出版者	IOP Publishing Ltd
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38354
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Zongmin
作者单位	1.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China 2.Chinese Acad Med Sci, Beijing Hosp, Inst Geriatr Med, Beijing 100730, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Chaozhi,Li, Bei,Xiao, Qian,et al. LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images[J]. PHYSICS IN MEDICINE AND BIOLOGY,2024,69(4):15.
APA	Yang, Chaozhi.,Li, Bei.,Xiao, Qian.,Bai, Yun.,Li, Yachuan.,...&Li, Hua.(2024).LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images.PHYSICS IN MEDICINE AND BIOLOGY,69(4),15.
MLA	Yang, Chaozhi,et al."LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images".PHYSICS IN MEDICINE AND BIOLOGY 69.4(2024):15.