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

TL;DR

This paper presents a data-centric machine learning approach that enhances optic nerve head segmentation and localization in retinal OCT images by leveraging domain knowledge to reduce spatial complexity, resulting in improved performance.

Contribution

It introduces a domain knowledge-driven spatial complexity reduction technique that improves U-Net based segmentation and localization of the optic nerve head in OCT en face images.

Findings

Improved segmentation accuracy with domain knowledge integration.

Enhanced localization performance in retinal OCT images.

Effective automated assessment of large retinal datasets.

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.