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

文献类型：期刊论文

作者Yang, Chaozhi3; Li, Bei2; Xiao, Qian3; Bai, Yun3; Li, Yachuan3; Li, Zongmin3; Li, Hongyi2; Li, Hua1
刊名PHYSICS IN MEDICINE AND BIOLOGY
出版日期2024-02-21
卷号69期号:4页码:15
关键词retinal vessel segmentation 3D-to-2D multi-scale layer attention reverse boundary attention OCTA volume
ISSN号0031-9155
DOI10.1088/1361-6560/ad2011
英文摘要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.
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
语种英语
WOS记录号WOS:001159796700001
出版者IOP Publishing Ltd
源URL[http://119.78.100.204/handle/2XEOYT63/38353]  
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Chinese Acad Med Sci, Beijing Hosp, Inst Geriatr Med, Beijing 100730, Peoples R China
3.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, 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.

入库方式： OAI收割

来源：计算技术研究所

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