The morphology and hierarchy of the vascular systems are essential for perfusion in supporting metabolism. In human retina, one of the most energy-demanding organs, retinal circulation nourishes the entire inner retina by an intricate vasculature emerging and remerging at the optic nerve head (ONH). Thus, tracing the vascular branching from ONH through the vascular tree can illustrate vascular hierarchy and allow detailed morphological quantification, and yet remains a challenging task. Here, we presented a novel approach for a robust semi-automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network (InSegNN). Distinct from semantic segmentation, InSegNN separates and labels different vascular trees individually and therefore enable tracing each tree throughout its branching. We have built-in three strategies to improve robustness and accuracy with temporal learning, spatial multi-sampling, and dynamic probability map. We achieved 83% specificity, and 50% improvement in Symmetric Best Dice (SBD) compared to literature, and outperformed baseline U-net. We have demonstrated tracing individual vessel trees from fundus images, and simultaneously retain the vessel hierarchy information. InSegNN paves a way for any subsequent morphological analysis of vascular morphology in relation to retinal diseases.

翻译：血管系统的形态与层次结构对支持新陈代谢的灌注至关重要。人眼视网膜是能量需求最高的器官之一，其血液循环通过从视神经头（ONH）发出并重新汇合的复杂血管网络滋养整个内层视网膜。因此，从视神经头出发沿血管树追踪分支结构，能够揭示血管层次并实现详细的形态学量化，但这仍是一项具有挑战性的任务。本文提出了一种新颖的鲁棒半自动血管追踪算法，基于实例分割神经网络（InSegNN）对人眼眼底图像进行处理。与语义分割不同，InSegNN能够分别标注并分离不同的血管树，从而实现对每棵血管树沿其分支的完整追踪。我们内置了三种策略来提升鲁棒性和准确性：时序学习、空间多采样以及动态概率图。与文献结果相比，本方法实现了83%的特异性，对称最佳Dice指标（SBD）提升了50%，并优于基线U-net模型。我们成功演示了从眼底图像中追踪单棵血管树，同时保留了血管层级信息。InSegNN为后续关于血管形态与视网膜疾病关系的形态学分析铺平了道路。