分割
计算机科学
人工智能
视网膜
计算机视觉
路径(计算)
特征(语言学)
相似性(几何)
模式识别(心理学)
投影(关系代数)
光学相干层析成像
图像(数学)
算法
光学
眼科
物理
医学
语言学
哲学
程序设计语言
作者
Chaozhi Yang,Bei Li,Qian Xiao,Yun Bai,Yachuan Li,Zongmin Li,Hongyi Li,Hua Li
标识
DOI:10.1088/1361-6560/ad2011
摘要
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.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.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.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. The code for LA-Net is available at https://github.com/y8421036/LA-Net.
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