  Macular holes are a common eye condition which result in visual impairment.
We look at the application of deep convolutional neural networks to the problem
of macular hole segmentation. We use the 3D U-Net architecture as a basis and
experiment with a number of design variants. Manually annotating and measuring
macular holes is time consuming and error prone. Previous automated approaches
to macular hole segmentation take minutes to segment a single 3D scan. Our
proposed model generates significantly more accurate segmentations in less than
a second. We found that an approach of architectural simplification, by greatly
simplifying the network capacity and depth, exceeds both expert performance and
state-of-the-art models such as residual 3D U-Nets.
