School of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia.
Lions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia.
J Optom. 2022;15 Suppl 1(Suppl 1):S58-S69. doi: 10.1016/j.optom.2022.11.001. Epub 2022 Nov 14.
Retinal and optic disc images are used to assess changes in the retinal vasculature. These can be changes associated with diseases such as diabetic retinopathy and glaucoma or induced using ophthalmodynamometry to measure arterial and venous pressure. Key steps toward automating the assessment of these changes are the segmentation and classification of the veins and arteries. However, such segmentation and classification are still required to be manually labelled by experts. Such automated labelling is challenging because of the complex morphology, anatomical variations, alterations due to disease and scarcity of labelled data for algorithm development. We present a deep machine learning solution called the multiscale guided attention network for retinal artery and vein segmentation and classification (MSGANet-RAV).
MSGANet-RAV was developed and tested on 383 colour clinical optic disc images from LEI-CENTRAL, constructed in-house and 40 colour fundus images from the AV-DRIVE public dataset. The datasets have a mean optic disc occupancy per image of 60.6% and 2.18%, respectively. MSGANet-RAV is a U-shaped encoder-decoder network, where the encoder extracts multiscale features, and the decoder includes a sequence of self-attention modules. The self-attention modules explore, guide and incorporate vessel-specific structural and contextual feature information to segment and classify central optic disc and retinal vessel pixels.
MSGANet-RAV achieved a pixel classification accuracy of 93.15%, sensitivity of 92.19%, and specificity of 94.13% on LEI-CENTRAL, outperforming several reference models. It similarly performed highly on AV-DRIVE with an accuracy, sensitivity and specificity of 95.48%, 93.59% and 97.27%, respectively.
The results show the efficacy of MSGANet-RAV for identifying central optic disc and retinal arteries and veins. The method can be used in automated systems designed to assess vascular changes in retinal and optic disc images quantitatively.
视网膜和视盘图像用于评估视网膜血管的变化。这些变化可能与糖尿病视网膜病变和青光眼等疾病有关，也可能通过眼动描记术来测量动脉和静脉压力而产生。实现这些变化自动评估的关键步骤是静脉和动脉的分割和分类。然而，这种分割和分类仍然需要专家手动标记。由于形态复杂、解剖变异、疾病引起的改变以及用于算法开发的标记数据稀缺，这种自动化标记具有挑战性。我们提出了一种称为多尺度引导注意力网络的深度学习解决方案，用于视网膜动脉和静脉分割和分类（MSGANet-RAV）。
MSGANet-RAV 是在 LEI-CENTRAL 构建的内部的 383 张彩色临床视盘图像和 AV-DRIVE 公共数据集的 40 张彩色眼底图像上开发和测试的。这些数据集的每张图像的视盘占有率平均值分别为 60.6%和 2.18%。MSGANet-RAV 是一个 U 形编码器-解码器网络，其中编码器提取多尺度特征，解码器包括一系列自注意力模块。自注意力模块探索、引导和合并血管特定的结构和上下文特征信息，以分割和分类中央视盘和视网膜血管像素。
MSGANet-RAV 在 LEI-CENTRAL 上实现了 93.15%的像素分类准确率、92.19%的灵敏度和 94.13%的特异性，优于几个参考模型。在 AV-DRIVE 上也表现出色，准确率、灵敏度和特异性分别为 95.48%、93.59%和 97.27%。
结果表明 MSGANet-RAV 用于识别中央视盘和视网膜动脉和静脉的有效性。该方法可用于设计自动系统，对视网膜和视盘图像中的血管变化进行定量评估。
