Precision in identifying and differentiating micro and macro blood vessels in the retina is crucial for the diagnosis of retinal diseases, although it poses a significant challenge. Current autoencoding-based segmentation approaches encounter limitations as they are constrained by the encoder and undergo a reduction in resolution during the encoding stage. The inability to recover lost information in the decoding phase further impedes these approaches. Consequently, their capacity to extract the retinal microvascular structure is restricted. To address this issue, we introduce Swin-Res-Net, a specialized module designed to enhance the precision of retinal vessel segmentation. Swin-Res-Net utilizes the Swin transformer which uses shifted windows with displacement for partitioning, to reduce network complexity and accelerate model convergence. Additionally, the model incorporates interactive fusion with a functional module in the Res2Net architecture. The Res2Net leverages multi-scale techniques to enlarge the receptive field of the convolutional kernel, enabling the extraction of additional semantic information from the image. This combination creates a new module that enhances the localization and separation of micro vessels in the retina. To improve the efficiency of processing vascular information, we've added a module to eliminate redundant information between the encoding and decoding steps. Our proposed architecture produces outstanding results, either meeting or surpassing those of other published models. The AUC reflects significant enhancements, achieving values of 0.9956, 0.9931, and 0.9946 in pixel-wise segmentation of retinal vessels across three widely utilized datasets: CHASE-DB1, DRIVE, and STARE, respectively. Moreover, Swin-Res-Net outperforms alternative architectures, demonstrating superior performance in both IOU and F1 measure metrics.

翻译：精准识别与区分视网膜中微血管与大血管对于视网膜疾病的诊断至关重要，然而这仍是一项重大挑战。当前基于自编码的分割方法存在局限性，受限于编码器并导致编码阶段分辨率下降。解码阶段无法恢复丢失信息的问题进一步阻碍了这些方法的效果，从而限制了对视网膜微血管结构的提取能力。为解决此问题，我们提出了Swin-Res-Net——一种专为提升视网膜血管分割精度设计的模块。该模块采用Swin Transformer，通过可位移的滑动窗口进行划分，以降低网络复杂度并加速模型收敛。此外，模型融合了Res2Net架构中的交互式功能模块。Res2Net利用多尺度技术扩大卷积核的感受野，从而提取图像中更多语义信息。这一组合形成了新型模块，显著增强了对视网膜微血管的定位与分离能力。为提升血管信息处理效率，我们还在编码与解码步骤间添加了冗余信息消除模块。所提出的架构在性能上达到或超越现有公开模型，取得了显著成果。在三个广泛使用的数据集CHASE-DB1、DRIVE和STARE上，像素级视网膜血管分割的AUC值分别达到0.9956、0.9931和0.9946，较其他模型有显著提升。此外，Swin-Res-Net在IOU和F1度量指标上均展现出超越其他架构的优越性能。