【摘要】 目的 针对视网膜血管图像分割中血管特征尺度多变、毛细血管细节丰富以及视杯视盘、病变等特殊区域干扰导致的表征不精确、分割误差大以及结果不准确等问题，提出一种视网膜血管图像分割的尺度特征表示学习网络，包括尺度特征表示、纹理特征增强和双重对比学习3个模块。方法 首先，输入视网膜图像集中的图像，通过引入空间自注意力构建尺度特征表示模块，对视网膜血管进行分层尺度表征；然后，采用上下文信息引导的纹理滤波器对血管尺度特征进行纹理特征增强；最后，通过采样血管尺度特征和纹理增强特征，并定义联合损失进行双重对比学习，优化两种特征空间中视杯视盘、病变等特殊区域的血管。结果 为了验证方法的有效性，在3个具有挑战性的数据集上进行对比实验，结果表明，构建的视网膜血管图像分割网络有助于准确表示血管尺度特征和纹理增强特征，能够较好地获得完整的视网膜毛细血管等特殊区域的血管分割结果。本文方法在DRIVE(digital retinal images for vessel extraction)数据集中较对比的大多数方法，Acc(accuracy)值平均提高了0.67%,Sp(specificity)值平均提高了0.48%；在STARE(structured analysis of the retina)数据集中较对比的大多数方法，Se(sensitivity)值平均提高了6.01%,Sp值平均提高了6.86%；在CHASE＿DB1(child heart and health study in England)数据集中较对比的大多数方法，Se值平均提高了1.88%,F1(F1 score)值平均提高了1.98%。结论 本文提出的视网膜血管图像分割网络，能精准分割多尺度血管、毛细血管和特殊区域的血管，有效辅助视网膜血管疾病诊断。更多还原

【Abstract】 Objective Retinal vessel image segmentation refers to the process of separating vessel pixels in a color fundus image from the background pixels. The morphology of retinal vessels is closely associated with various ophthalmic diseases and plays a crucial role in computer-aided diagnosis and smart medicine. Additionally, retinal vessel images provide important biological information that can be used as a basis for personal identification systems in the field of social security. Furthermore, segmented retinal vessel images can serve as a priori for other anatomical sites, such as the macula. Currently, retinal image segmentation methods can be categorized into traditional and deep learning methods. Existing methods for retinal vessel image segmentation demonstrate good performance in segmenting large-scale vessels, primarily due to the ease of capturing features related to these prominent structures. Particularly, U-Net can effectively handle the complicated anatomical semantics involved in retinal vessel segmentation tasks, fusing adjacent-level features to learn additional local and global semantic information for highly accurate segmentation. Although remarkable progress has been made in retinal vessel segmentation with the advancement of deep learning, several challenging issues remain. First, current methods do not adequately represent vessels feature at multiple scales, resulting in poor segmentation results for retinal vessels with large differences in size and shape. Second, thin vessels, particularly those located at the ends of extremely low-contrast branches, are easily missed by current methods, resulting in incomplete vessel segmentation. Additionally, the medical semantics surrounding retinal vessels are complex. Specific regions, such as the optic cup, optic disc, and lesions, can interfere with vessel segmentation and seriously affect the accuracy of retinal vessel segmentation. Moreover, most images in the STARE dataset have severe lesions, and the information in different datasets notably varies, resulting in lower sensitivity of vessel segmentation results. To address these issues, a scale feature representation learning network for retinal vessel image segmentation is proposed by introducing the following three modules: scale feature representation, texture feature enhancement, and double contrastive learning.Method In this study, the images are first inputted into the retinal image set, and the initial layer of retinal vessel features is extracted using a U-Net-based backbone network. Hierarchical representation and stepwise fusion strategies are employed to fully capture the scale features of the vessels. This strategy is realized by introducing average pool operations and a spatial self-attention mechanism, which enriches multiscale information and generate a vessel scale feature representation. Then, based on the vessel scale feature, the last four layers of scaleencoded features are obtained through downsampling. During the skip connection process, the scale-encoded features are combined with deeper features to create intermediate features using three types of convolutions. These intermediate features are further enhanced using contextual information-guided texture filters, resulting in enhanced texture features that effectively focus on the edges of thin vessels by supplementing texture information. Finally, the vessel scale and textureenhanced features are sampled to obtain vessel pixels, background pixels near vessels, and other background pixels in the two feature space domains. These samples are used as inputs for double constraint learning to calculate the double constraint loss. Double constraint learning helps reduce intra-class distance, increase inter-class variance, and substantially improve the segmentation of thin vessels and vessels in specific regions, such as the optic cup, optic disc, or lesions regions.Result The illustrated method is validated on three challenging retinal vessel image datasets: digital retinal images for vessel extraction(DRIVE), structured analysis of the retina(STARE), and child heart and health study in England(CHASE＿DB1). The accuracy(Acc), sensitivity(Se), specificity(Sp) on the STARE and CHASE＿DB1 datasets are(0. 976 5, 0. 841 5 and 0. 987 4) and(0. 978 4, 0. 886 4 and 0. 992 3), respectively. These results indicate that the proposed method outperforms most competing methods and remarkably improves performance in extracting thin vessels in regions with lesions or near the optic disc. Compared with other methods, the Acc of the proposed method in the DRIVE dataset is increased by 0. 67%, while the Sp is improved by an average of 0. 48%. In the STARE dataset, the Se value is increased by 6. 01% and the Sp value is increased by 6. 86% on average. In the CHASE＿DB1 dataset, the Se value is increased by 1. 88%, and the F1 score(F1) value is improved by 1. 98% compared to other methods. The advantages of the method are visually analyzed by demonstrating the vessel details, demonstrating its capability to achieve better results for thin vessels with fewer vessel breaks. Additionally, improved results are obtained in dark and unevenly illuminated images. Some existing methods struggle with inaccurate segmentation results for crossed vessels at the optic disc and often fail to segment thin vessels around this area. In contrast, the segmentation results of the proposed method for crossed vessels at the optic disc do not show the phenomenon of vessel rupture. Notably improvements are observed in the segmentation of vessels and thin vessels in regions such as the optic disc, lesions, and other regions. The results indicate that the constructed retinal vessel image segmentation network effectively represents the scale features of vessels and enhances their texture, enabling accurate segmentation of complete retinal capillaries, especially in challenging regions. Finally, the findings from ablation studies and analyses demonstrate that the combination of the three modules is essential for simultaneously addressing the variable scale and anatomical semantic variations of retinal vessels.Conclusion This paper presents a retinal vessel image segmentation network that accurately segments multiscale vessels, thin vessels, and vessels in specialized regions by effectively representing scale features and enhancing texture features, thereby assisting in the diagnosis of vascular diseases.更多还原