  Microvascular anatomy is known to be involved in various neurological
disorders. However, understanding these disorders is hindered by the lack of
imaging modalities capable of capturing the comprehensive three-dimensional
vascular network structure at microscopic resolution. With a lateral resolution
of $<=$20 {\textmu}m and ability to reconstruct large tissue blocks up to tens
of cubic centimeters, serial-section optical coherence tomography (sOCT) is
well suited for this task. This method uses intrinsic optical properties to
visualize the vessels and therefore does not possess a specific contrast, which
complicates the extraction of accurate vascular models. The performance of
traditional vessel segmentation methods is heavily degraded in the presence of
substantial noise and imaging artifacts and is sensitive to domain shifts,
while convolutional neural networks (CNNs) require extensive labeled data and
are also sensitive the precise intensity characteristics of the data that they
are trained on. Building on the emerging field of synthesis-based training,
this study demonstrates a synthesis engine for neurovascular segmentation in
sOCT images. Characterized by minimal priors and high variance sampling, our
highly generalizable method tested on five distinct sOCT acquisitions
eliminates the need for manual annotations while attaining human-level
precision. Our approach comprises two phases: label synthesis and
label-to-image transformation. We demonstrate the efficacy of the former by
comparing it to several more realistic sets of training labels, and the latter
by an ablation study of synthetic noise and artifact models.
