Wang Siqi, Yu Xiaosheng, Wu Hao, Wang Ying, Wu Chengdong
College of Robot Science and Engineering, Northeastern University, Shenyang, 110170, Liaoning, China.
Faculty of Computer Science, Macquarie University, Macquarie Park, 2109, Sydney, Australia.
Med Biol Eng Comput. 2025 Apr;63(4):1161-1176. doi: 10.1007/s11517-024-03256-z. Epub 2024 Dec 13.
Optical coherence tomography angiography (OCTA) is a novel non-invasive retinal vessel imaging technique that can display high-resolution 3D vessel structures. The quantitative analysis of retinal vessel morphology plays an important role in the automatic screening and diagnosis of fundus diseases. The existing segmentation methods struggle to effectively use the 3D volume data and 2D projection maps of OCTA images simultaneously, which leads to problems such as discontinuous microvessel segmentation results and deviation of morphological estimation. To enhance diagnostic support for fundus diseases, we propose a cross-dimensional modal fusion network (CMFNet) using both 3D volume data and 2D projection maps for accurate OCTA vessel segmentation. Firstly, we use different encoders to generate 2D projection features and 3D volume data features from projection maps and volume data, respectively. Secondly, we design an attentional cross-feature projection learning module to purify 3D volume data features and learn its projection features along the depth direction. Then, we develop a cross-dimensional hierarchical fusion module to effectively fuse coded features learned from the volume data and projection maps. In addition, we extract high-level semantic weight information and map it to the cross-dimensional hierarchical fusion process to enhance fusion performance. To validate the efficacy of our proposed method, we conducted experimental evaluations using the publicly available dataset: OCTA-500. The experimental results show that our method achieves state-of-the-art performance.
光学相干断层扫描血管造影（OCTA）是一种新型的非侵入性视网膜血管成像技术，能够显示高分辨率的三维血管结构。视网膜血管形态的定量分析在眼底疾病的自动筛查和诊断中起着重要作用。现有的分割方法难以同时有效地利用OCTA图像的三维体数据和二维投影图，这导致了微血管分割结果不连续、形态估计偏差等问题。为了增强对眼底疾病的诊断支持，我们提出了一种跨维度模态融合网络（CMFNet），它同时使用三维体数据和二维投影图来进行准确的OCTA血管分割。首先，我们使用不同的编码器分别从投影图和体数据中生成二维投影特征和三维体数据特征。其次，我们设计了一个注意力交叉特征投影学习模块来纯化三维体数据特征，并学习其沿深度方向的投影特征。然后，我们开发了一个跨维度分层融合模块，以有效地融合从体数据和投影图中学习到的编码特征。此外，我们提取高级语义权重信息并将其映射到跨维度分层融合过程中，以提高融合性能。为了验证我们提出的方法的有效性，我们使用公开可用的数据集OCTA-500进行了实验评估。实验结果表明，我们的方法达到了当前的最优性能。
