  You can have your cake and eat it too. Microvessel segmentation in optical
coherence tomography angiography (OCTA) images remains challenging.
Skeleton-level segmentation shows clear topology but without diameter
information, while pixel-level segmentation shows a clear caliber but low
topology. To close this gap, we propose a novel label adversarial learning
(LAL) for skeleton-level to pixel-level adjustable vessel segmentation. LAL
mainly consists of two designs: a label adversarial loss and an embeddable
adjustment layer. The label adversarial loss establishes an adversarial
relationship between the two label supervisions, while the adjustment layer
adjusts the network parameters to match the different adversarial weights. Such
a design can efficiently capture the variation between the two supervisions,
making the segmentation continuous and tunable. This continuous process allows
us to recommend high-quality vessel segmentation with clear caliber and
topology. Experimental results show that our results outperform manual
annotations of current public datasets and conventional filtering effects.
Furthermore, such a continuous process can also be used to generate an
uncertainty map representing weak vessel boundaries and noise.
