SD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors.

Botond Fazekas¹, Guilherme Aresta², Dmitrii Lachinov², Sophie Riedl³, Julia Mai³, Ursula Schmidt-Erfurth³, Hrvoje Bogunović²

¹Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria. Electronic address: botond.fazekas@meduniwien.ac.at.

²Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

³Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.

PubMed DOI

Abstract

BACKGROUND AND OBJECTIVES: Automated, anatomically coherent retinal layer segmentation in optical coherence tomography (OCT) is one of the most important components of retinal disease management. However, current methods rely on large amounts of labeled data, which can be difficult and expensive to obtain. In addition, these systems tend often propose anatomically impossible results, which undermi…

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