  The automatic detection and localization of anatomical features in retinal
imaging data are relevant for many aspects. In this work, we follow a
data-centric approach to optimize classifier training for optic nerve head
detection and localization in optical coherence tomography en face images of
the retina. We examine the effect of domain knowledge driven spatial complexity
reduction on the resulting optic nerve head segmentation and localization
performance. We present a machine learning approach for segmenting optic nerve
head in 2D en face projections of 3D widefield swept source optical coherence
tomography scans that enables the automated assessment of large amounts of
data. Evaluation on manually annotated 2D en face images of the retina
demonstrates that training of a standard U-Net can yield improved optic nerve
head segmentation and localization performance when the underlying pixel-level
binary classification task is spatially relaxed through domain knowledge.
