Husvogt L, Yaghy A, Camacho A, Lam K, Schottenhamml J, Ploner S, Fujimoto JG, Waheed NK, Maier A (2024)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2024
Book Volume: 14
Article Number: 21520
DOI: 10.1038/s41598-024-72375-2
Open Access Link: https://rdcu.be/dT0FK
Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.
APA:
Husvogt, L., Yaghy, A., Camacho, A., Lam, K., Schottenhamml, J., Ploner, S.,... Maier, A. (2024). Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-72375-2
MLA:
Husvogt, Lennart, et al. "Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy." Scientific Reports 14 (2024).
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