SD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors
A novel, robust, label-efficient retinal layer segmentation method was proposed. The approach has shown state-of-the-art layer segmentation performance with a fraction of the training data available, while at the same time, its robustness against domain shift was also shown.
Authors
- Botond Fazekas (ORCID: https://orcid.org/0000-0001-6158-2612)
- Sophie Riedl (ORCID: https://orcid.org/0000-0003-0003-0886)
- Dmitrii Lachinov
- Julia Mai (ORCID: https://orcid.org/0000-0002-0528-9742)
- Guilherme Aresta (ORCID: https://orcid.org/0000-0002-4225-2156)
- Hrvoje Bogunović (ORCID: https://orcid.org/0000-0002-9168-0894)
- Ursula Schmidt-Erfurth
Institutions
- Austrian Research Institute for Artificial Intelligence (AT)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108586
- Citations
- 4
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Christian Doppler Forschungsgesellschaft
- Bundesministerium für Digitalisierung und Wirtschaftsstandort