  Glaucoma is the leading cause of irreversible blindness in the world,
affecting over 70 million people. The cumbersome Standard Automated Perimetry
(SAP) test is most frequently used to detect visual loss due to glaucoma. Due
to the SAP test's innate difficulty and its high test-retest variability, we
propose the RetiNerveNet, a deep convolutional recursive neural network for
obtaining estimates of the SAP visual field. RetiNerveNet uses information from
the more objective Spectral-Domain Optical Coherence Tomography (SDOCT).
RetiNerveNet attempts to trace-back the arcuate convergence of the retinal
nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness
around the optic disc, to estimate individual age-corrected 24-2 SAP values.
Recursive passes through the proposed network sequentially yield estimates of
the visual locations progressively farther from the optic disc. While all the
methods used for our experiments exhibit lower performance for the advanced
disease group, the proposed network is observed to be more accurate than all
the baselines for estimating the individual visual field values. We further
augment RetiNerveNet to additionally predict the SAP Mean Deviation values and
also create an ensemble of RetiNerveNets that further improves the performance,
by increasingly weighting-up underrepresented parts of the training data.
