  Optical coherence tomography (OCT) is a non-invasive imaging technique widely
used for ophthalmology. It can be extended to OCT angiography (OCT-A), which
reveals the retinal vasculature with improved contrast. Recent deep learning
algorithms produced promising vascular segmentation results; however, 3D
retinal vessel segmentation remains difficult due to the lack of manually
annotated training data. We propose a learning-based method that is only
supervised by a self-synthesized modality named local intensity fusion (LIF).
LIF is a capillary-enhanced volume computed directly from the input OCT-A. We
then construct the local intensity fusion encoder (LIFE) to map a given OCT-A
volume and its LIF counterpart to a shared latent space. The latent space of
LIFE has the same dimensions as the input data and it contains features common
to both modalities. By binarizing this latent space, we obtain a volumetric
vessel segmentation. Our method is evaluated in a human fovea OCT-A and three
zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on
human data and 0.8594 +/- 0.0275 on zebrafish data, a dramatic improvement over
existing unsupervised algorithms.
