Background and objectives

The study of the retinal vasculature represents a fundamental stage in the screening and diagnosis of many high-incidence diseases, both systemic and ophthalmic. A complete retinal vascular analysis requires the segmentation of the vascular tree along with the classification of the blood vessels into arteries and veins. Early automatic methods approach these complementary segmentation and classification tasks in two sequential stages. However, currently, these two tasks are approached as a joint semantic segmentation, because the classification results highly depend on the effectiveness of the vessel segmentation. In that regard, we propose a novel approach for the simultaneous segmentation and classification of the retinal arteries and veins from eye fundus images.

Methods

We propose a novel method that, unlike previous approaches, and thanks to the proposal of a novel loss, decomposes the joint task into three segmentation problems targeting arteries, veins and the whole vascular tree. This configuration allows to handle vessel crossings intuitively and directly provides accurate segmentation masks of the different target vascular trees.

Results

The provided ablation study on the public Retinal Images vessel Tree Extraction (RITE) dataset demonstrates that the proposed method provides a satisfactory performance, particularly in the segmentation of the different structures. Furthermore, the comparison with the state of the art shows that our method achieves highly competitive results in the artery/vein classification, while significantly improving the vascular segmentation.

Conclusions

The proposed multi-segmentation method allows to detect more vessels and better segment the different structures, while achieving a competitive classification performance. Also, in these terms, our approach outperforms the approaches of various reference works. Moreover, in contrast with previous approaches, the proposed method allows to directly detect the vessel crossings, as well as preserving the continuity of both arteries and veins at these complex locations.

中文翻译：

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从彩色眼底图像中同时分割和分类视网膜动静脉

背景和目标

视网膜脉管系统的研究代表了许多全身性和眼科高发疾病的筛查和诊断的基本阶段。完整的视网膜血管分析需要对血管树进行分割以及将血管分类为动脉和静脉。早期的自动方法在两个连续阶段处理这些互补的分割和分类任务。然而，目前，这两个任务被视为联合语义分割，因为分类结果高度依赖于血管分割的有效性。在这方面，我们提出了一种从眼底图像同时分割和分类视网膜动脉和静脉的新方法。

方法

我们提出了一种新方法，与以前的方法不同，由于提出了一种新的损失，将联合任务分解为针对动脉、静脉和整个血管树的三个分割问题。这种配置允许直观地处理血管交叉并直接提供不同目标血管树的准确分割掩码。

结果

提供的对公共视网膜图像血管树提取（RITE）数据集的消融研究表明，所提出的方法提供了令人满意的性能，特别是在不同结构的分割方面。此外，与现有技术的比较表明，我们的方法在动脉/静脉分类中取得了极具竞争力的结果，同时显着改善了血管分割。

结论

所提出的多分割方法允许检测更多的血管并更好地分割不同的结构，同时实现具有竞争力的分类性能。此外，在这些方面，我们的方法优于各种参考著作的方法。此外，与以前的方法相比，所提出的方法允许直接检测血管交叉，并在这些复杂位置保持动脉和静脉的连续性。