Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images

Abstract

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.

Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

“…For research teams, this can practically mean, for example, reducing the spatial complexity in an image segmentation task to the relevant image regions rather than keeping on tuning the devised model architecture, model complexity, data augmentation strategies, or related training strategies [60].…”