Background and objectives: Retinal vessel segmentation (RVS) helps in diagnosing diseases such as hypertension, cardiovascular diseases, and others. Convolutional neural networks are widely used in RVS tasks. However, how to comprehensively evaluate the segmentation results and how to improve the networks’ learning ability are two great challenges.

Methods: In this paper, we proposed an ingenious index: fusion score (FS), which provides an overall measure for those binary images. The FS converts multiple metrics into a single target, and therefore facilitates the optimal threshold’s selection and models’ comparison. In addition, We simultaneously combined size-invariant feature maps and dense connectivity together to improve the traditional CNN’s learning ability. Therefore, a size-invariant convolutional network with dense connectivity is designed for RVS. The size-invariant skill helps the deep layers create feature maps with high resolution. The dense connectivity technique is utilized to integrate those hierarchical features and reuse characteristic maps to enhance the network’s learning ability. Finally, an optimized threshold is used on the output image to obtain a binary image.

Results: The results of experiments conducted on two shared retinal image databases, DRIVE and STARE, demonstrate that our approach outperforms other techniques when evaluated in terms of F1-score, Matthews correlation coefficient (MCC), G-mean and FS. In addition, the cross training reveals that our method has stronger robustness with respect to training sets. Segmenting a 565 × 584 image only takes 39 ms with a single GPU (graphics processing unit).

Conclusions: Compared with those traditional metrics, the FS is a better indicator to measure the results of RVS tasks. The experimental results revealed that the proposed method is more suitable for real-world applications.

背景和目的：视网膜血管分割（RVS）有助于诊断诸如高血压，心血管疾病等疾病。卷积神经网络广泛用于RVS任务。然而，如何综合评估分割结果以及如何提高网络的学习能力是两个重大挑战。

方法：在本文中，我们提出了一个独创的索引：融合分数（FS），它为这些二值图像提供了一种总体度量。FS将多个度量标准转换为单个目标，因此有助于优化最佳阈值的选择和模型的比较。此外，我们同时将尺寸不变的特征图和密集的连接性结合在一起，以提高传统CNN的学习能力。因此，为RVS设计了具有密集连接性的大小不变的卷积网络。尺寸不变的技能可帮助深层创建高分辨率的特征图。密集连接技术用于集成那些分层功能并重用特性图，以增强网络的学习能力。最后，在输出图像上使用优化的阈值以获得二进制图像。

结果：在两个共享的视网膜图像数据库DRIVE和STARE上进行的实验结果表明，在F1得分，Matthews相关系数（MCC），G均值和FS方面进行评估时，我们的方法优于其他技术。此外，交叉训练表明我们的方法相对于训练集具有更强的鲁棒性。使用单个GPU（图形处理单元）分割565×584图像仅需39 ms。

结论：与传统指标相比，FS是衡量RVS任务结果的更好指标。实验结果表明，该方法更适合实际应用。