  Projection map (PM) from optical coherence tomography (OCT) B-scan is an
important tool to diagnose retinal diseases, which typically requires retinal
layer segmentation. In this study, we present a novel end-to-end framework to
predict PMs from B-scans. Instead of segmenting retinal layers explicitly, we
represent them implicitly as predicted coordinates. By pixel interpolation on
uniformly sampled coordinates between retinal layers, the corresponding PMs
could be easily obtained with pooling. Notably, all the operators are
differentiable; therefore, this Differentiable Projection Module (DPM) enables
end-to-end training with the ground truth of PMs rather than retinal layer
segmentation. Our framework produces high-quality PMs, significantly
outperforming baselines, including a vanilla CNN without DPM and an
optimization-based DPM without a deep prior. Furthermore, the proposed DPM, as
a novel neural representation of areas/volumes between curves/surfaces, could
be of independent interest for geometric deep learning.
