  Segmentation of blood vessels in murine cerebral 3D OCTA images is
foundational for in vivo quantitative analysis of the effects of neurovascular
disorders, such as stroke or Alzheimer's, on the vascular network. However, to
accurately segment blood vessels with state-of-the-art deep learning methods, a
vast amount of voxel-level annotations is required. Since cerebral 3D OCTA
images are typically plagued by artifacts and generally have a low
signal-to-noise ratio, acquiring manual annotations poses an especially
cumbersome and time-consuming task. To alleviate the need for manual
annotations, we propose utilizing synthetic data to supervise segmentation
algorithms. To this end, we extract patches from vessel graphs and transform
them into synthetic cerebral 3D OCTA images paired with their matching ground
truth labels by simulating the most dominant 3D OCTA artifacts. In extensive
experiments, we demonstrate that our approach achieves competitive results,
enabling annotation-free blood vessel segmentation in cerebral 3D OCTA images.
