  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.
