Retinal fundus images can be an invaluable diagnosis tool for screening epidemic diseases like hypertension or diabetes. And they become especially useful when the arterioles and venules they depict are clearly identified and annotated. However, manual annotation of these vessels is extremely time demanding and taxing, which calls for automatic segmentation. Although convolutional neural networks can achieve high overlap between predictions and expert annotations, they often fail to produce topologically correct predictions of tubular structures. This situation is exacerbated by the bifurcation versus crossing ambiguity which causes classification mistakes. This paper shows that including a topology preserving term in the loss function improves the continuity of the segmented vessels, although at the expense of artery-vein misclassification and overall lower overlap metrics. However, we show that by including an orientation score guided convolutional module, based on the anisotropic single sided cake wavelet, we reduce such misclassification and further increase the topology correctness of the results. We evaluate our model on public datasets with conveniently chosen metrics to assess both overlap and topology correctness, showing that our model is able to produce results on par with state-of-the-art from the point of view of overlap, while increasing topological accuracy.

翻译：眼底图像可作为筛查高血压、糖尿病等流行性疾病的重要诊断工具，尤其在清晰识别并标注视网膜小动脉与小静脉时更为有效。然而，人工标注这些血管极其耗时费力，因此亟需自动化分割方法。尽管卷积神经网络能实现预测结果与专家标注的高重叠度，但其对管状结构的预测常缺乏拓扑正确性。分叉与交叉的混淆问题进一步加剧了分类错误。本文表明，在损失函数中加入拓扑保持项可提升分割血管的连续性，但这会以牺牲动静脉分类准确性并降低整体重叠指标为代价。然而，通过引入基于各向异性单边蛋糕小波的导向评分卷积模块，我们能够减少此类分类错误，并进一步提高结果的拓扑正确性。我们在公开数据集上采用兼顾重叠度与拓扑正确性的指标进行评估，结果显示，本模型在保持与现有最优方法相当的重叠性能的同时，显著提升了拓扑准确性。