  Optical coherence tomography angiography (OCTA) is a novel imaging modality
that allows a micron-level resolution to present the three-dimensional
structure of the retinal vascular. In our previous work, a 3D-to-2D image
projection network (IPN) was proposed for retinal vessel (RV) and foveal
avascular zone (FAZ) segmentations in OCTA images. One of its advantages is
that the segmentation results are directly from the original volumes without
using any projection images and retinal layer segmentation. In this work, we
propose image projection network V2 (IPN-V2), extending IPN by adding a plane
perceptron to enhance the perceptron ability in the horizontal direction. We
also propose IPN-V2+, as a supplement of the IPN-V2, by introducing a global
retraining process to overcome the "checkerboard effect". Besides, we propose a
new multi-modality dataset, dubbed OCTA-500. It contains 500 subjects with two
field of view (FOV) types, including OCT and OCTA volumes, six types of
projections, four types of text labels and two types of pixel-level labels. The
dataset contains more than 360K images with a size of about 80GB. To the best
of our knowledge, it is currently the largest OCTA dataset with the abundant
information. Finally, we perform a thorough evaluation of the performance of
IPN-V2 on the OCTA-500 dataset. The experimental results demonstrate that our
proposed IPN-V2 performs better than IPN and other deep learning methods in RV
segmentation and FAZ segmentation.
