Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Evaluating the real-world accessibility of open-source deep learning models for retinal vessel segmentation in optical coherence tomography angiography
Related
- Serial bedside retinal optical coherence tomography angiography in sepsis associated circulatory dysfunction
- Binarization Parameters Affect Quantitative Choriocapillaris Biomarkers Derived from Optical Coherence Tomography Angiography
- Optical Coherence Tomography‐Guided Versus Angiography‐Guided Coronary Stent Implantation: An Updated Meta‐Analysis
- A review of the optical coherence tomography angiography in neuro-ophthalmology
- Establishing a simulated chronic total occlusion model for laserwire device testing using angiography, optical coherence tomography and histopathological analysis
BibTeX
@article{liu2026evaluating,
title = {{Evaluating the real-world accessibility of open-source deep learning models for retinal vessel segmentation in optical coherence tomography angiography}},
author = {Dayu Liu and Madeleine S. Torcasso and David Alonso-Caneiro and Gavrielle R. Untracht and Jocelyn J. Drinkwater and Qiang Li and Elaine Ong and Jeremiah K. H. Lim and Angus W. Turner and Fred K. Chen and Danuta M. Sampson},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-75895-1},
url = {https://www.nature.com/articles/s41598-026-75895-1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Evaluating the real-world accessibility of open-source deep learning models for retinal vessel segmentation in optical coherence tomography angiography
AU - Dayu Liu
AU - Madeleine S. Torcasso
AU - David Alonso-Caneiro
AU - Gavrielle R. Untracht
AU - Jocelyn J. Drinkwater
AU - Qiang Li
AU - Elaine Ong
AU - Jeremiah K. H. Lim
AU - Angus W. Turner
AU - Fred K. Chen
AU - Danuta M. Sampson
PY - 2026
DA - 2026/10/27
JO - Scientific Reports
DO - 10.1038/s41598-026-75895-1
UR - https://www.nature.com/articles/s41598-026-75895-1
SN - 2045-2322
ER -