  In this work, we propose a Neural Architecture Search (NAS) for retinal layer
segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the
Unet architecture in the NAS framework as its backbone for the segmentation of
the retinal layers in our collected and pre-processed OCT image dataset. At the
pre-processing stage, we conduct super resolution and image processing
techniques on the raw OCT scans to improve the quality of the raw images. For
our search strategy, different primitive operations are suggested to find the
down- & up-sampling cell blocks, and the binary gate method is applied to make
the search strategy practical for the task in hand. We empirically evaluated
our method on our in-house OCT dataset. The experimental results demonstrate
that the self-adapting NAS-Unet architecture substantially outperformed the
competitive human-designed architecture by achieving 95.4% in mean Intersection
over Union metric and 78.7% in Dice similarity coefficient.
