Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy.

Autor: Husvogt, Lennart, Yaghy, Antonio, Camacho, Alex, Lam, Kenneth, Schottenhamml, Julia, Ploner, Stefan B., Fujimoto, James G., Waheed, Nadia K., Maier, Andreas
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Zdroj: Scientific Reports; 9/14/2024, Vol. 14 Issue 1, p1-13, 13p
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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