Retinal vascular segmentation, is a widely researched subject in biomedical image processing, aims to relieve ophthalmologists' workload when treating and detecting retinal disorders. However, segmenting retinal vessels has its own set of challenges, with prior techniques failing to generate adequate results when segmenting branches and microvascular structures. The neural network approaches used recently are characterized by the inability to keep local and global properties together and the failure to capture tiny end vessels make it challenging to attain the desired result. To reduce this retinal vessel segmentation problem, we propose a full-scale micro-vessel extraction mechanism based on an encoder-decoder neural network architecture, sigmoid smoothing, and an adaptive threshold method. The network consists of of residual, encoder booster, bottleneck enhancement, squeeze, and excitation building blocks. All of these blocks together help to improve the feature extraction and prediction of the segmentation map. The proposed solution has been evaluated using the DRIVE, CHASE-DB1, and STARE datasets, and competitive results are obtained when compared with previous studies. The AUC and accuracy on the DRIVE dataset are 0.9884 and 0.9702, respectively. On the CHASE-DB1 dataset, the scores are 0.9903 and 0.9755, respectively. On the STARE dataset, the scores are 0.9916 and 0.9750, respectively. The performance achieved is one step ahead of what has been done in previous studies, and this results in a higher chance of having this solution in real-life diagnostic centers that seek ophthalmologists attention.

翻译：视网膜血管分割是生物医学图像处理中广泛研究的课题，旨在减轻眼科医生在诊疗视网膜疾病时的工作负担。然而，视网膜血管分割面临独特挑战，现有技术在分割分支与微血管结构时难以产生理想结果。近期采用的神经网络方法存在无法兼顾局部与全局特征、难以捕捉细微末端血管的局限性，导致难以达到预期效果。为缓解这一视网膜血管分割难题，我们提出基于编码器-解码器神经网络架构、Sigmoid平滑与自适应阈值方法的全尺度微血管提取机制。该网络由残差块、编码器增强块、瓶颈增强块、压缩块与激励块构成，这些模块共同提升了特征提取与分割图预测能力。我们利用DRIVE、CHASE-DB1和STARE数据集对所提方案进行评估，与既有研究相比取得了具有竞争力的结果。在DRIVE数据集上的AUC与准确率分别为0.9884与0.9702，CHASE-DB1数据集上为0.9903与0.9755，STARE数据集上为0.9916与0.9750。所实现的性能较先前研究更进一步，显著提升了该方案在寻求眼科诊疗的真实诊断中心中的应用前景。