  Serial sectioning Optical Coherence Tomography (sOCT) is a high-throughput,
label free microscopic imaging technique that is becoming increasingly popular
to study post-mortem neurovasculature. Quantitative analysis of the vasculature
requires highly accurate segmentation; however, sOCT has low
signal-to-noise-ratio and displays a wide range of contrasts and artifacts that
depend on acquisition parameters. Furthermore, labeled data is scarce and
extremely time consuming to generate. Here, we leverage synthetic datasets of
vessels to train a deep learning segmentation model. We construct the vessels
with semi-realistic splines that simulate the vascular geometry and compare our
model with realistic vascular labels generated by constrained constructive
optimization. Both approaches yield similar Dice scores, although with very
different false positive and false negative rates. This method addresses the
complexity inherent in OCT images and paves the way for more accurate and
efficient analysis of neurovascular structures.
