The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness, and real-time efficacy. To comprehensively address these challenges, a new U-Net-like, lightweight Transformer network for retinal vessel segmentation is presented. By integrating MobileViT+ and a novel local representation in the encoder, our design emphasizes lightweight processing while capturing intricate image structures, enhancing vessel edge precision. A novel joint loss is designed, leveraging the characteristics of weighted cross-entropy and Dice loss to effectively guide the model through the task's challenges, such as foreground-background imbalance and intricate vascular structures. Exhaustive experiments were performed on three prominent retinal image databases. The results underscore the robustness and generalizability of the proposed LiViT-Net, which outperforms other methods in complex scenarios, especially in intricate environments with fine vessels or vessel edges. Importantly, optimized for efficiency, LiViT-Net excels on devices with constrained computational power, as evidenced by its fast performance. To demonstrate the model proposed in this study, a freely accessible and interactive website was established (), revealing real-time performance with no login requirements.

中文翻译：

---

LiViT-Net：一种类似 U-Net 的轻量级 Transformer 网络，用于视网膜血管分割

从图像中精确分割视网膜血管的复杂任务对于诊断各种眼部疾病至关重要，但由于尺度变化、复杂的解剖模式、低对比度和训练数据的限制等因素，给模型带来了巨大的挑战。基于这些挑战，我们提供了涵盖模型架构、损失函数设计、鲁棒性和实时功效的新颖贡献。为了全面应对这些挑战，提出了一种新的类似 U-Net 的轻量级 Transformer 网络，用于视网膜血管分割。通过在编码器中集成 MobileViT+ 和新颖的局部表示，我们的设计强调轻量级处理，同时捕获复杂的图像结构，提高血管边缘精度。设计了一种新颖的联合损失，利用加权交叉熵和 Dice 损失的特征来有效指导模型应对任务挑战，例如前景背景不平衡和复杂的血管结构。在三个著名的视网膜图像数据库上进行了详尽的实验。结果强调了所提出的 LiViT-Net 的鲁棒性和通用性，它在复杂场景中优于其他方法，特别是在具有精细血管或血管边缘的复杂环境中。重要的是，LiViT-Net 针对效率进行了优化，在计算能力有限的设备上表现出色，其快速性能就证明了这一点。为了演示本研究中提出的模型，建立了一个可免费访问的交互式网站（），无需登录即可显示实时性能。