Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images

TL;DR

This paper introduces a fully automated machine learning method using a residual U-Net to accurately segment hyperreflective foci in retinal OCT images, aiding disease monitoring across various retinal conditions.

Contribution

The study presents a novel automated segmentation approach for HRF in SD-OCT scans, demonstrating high accuracy across multiple retinal diseases.

Findings

High segmentation accuracy achieved with residual U-Net

Method effective across different retinal diseases

Automates a previously manual and error-prone process

Abstract

The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The presence of hyperreflective foci (HRF) is related to disease progression in various retinal diseases. Manual identification of HRF in spectral-domain optical coherence tomography (SD-OCT) scans is error-prone and tedious. We present a fully automated machine learning approach for segmenting HRF in SD-OCT scans. Evaluation on annotated OCT images of the retina demonstrates that a residual U-Net allows to segment HRF with high accuracy. As our dataset comprised data from different retinal diseases including age-related macular degeneration, diabetic macular edema and retinal vein occlusion, the algorithm can safely be applied in all of them though different pathophysiological origins are known.