University of Osnabrück, Germany.
Department of Ophthalmology, University of Münster Medical Center, Germany.
Stud Health Technol Inform. 2023 May 18;302:581-585. doi: 10.3233/SHTI230209.
Glaucoma is one of the leading causes of blindness worldwide. Therefore, early detection and diagnosis are key to preserve full vision in patients. As part of the SALUS study, we create a blood vessel segmentation model based on U-Net. We trained U-Net on three different loss functions and used hyperparameter tuning to find their optimal hyperparameters for each loss function. The best models for each of the loss functions achieved an accuracy of over 93%, Dice scores around 83% and Intersection over Union scores over 70%. They each identify large blood vessels reliably and even recognize smaller blood vessels in the retinal fundus images and thus pave the way for improved glaucoma management.
青光眼是全球导致失明的主要原因之一。因此，早期发现和诊断对于保护患者的全视力至关重要。作为 SALUS 研究的一部分，我们基于 U-Net 创建了一个血管分割模型。我们使用三种不同的损失函数对 U-Net 进行了训练，并使用超参数调优找到了每种损失函数的最佳超参数。每种损失函数的最佳模型的准确率都超过了 93%，Dice 分数约为 83%，交并比分数超过 70%。它们都能够可靠地识别大血管，甚至可以识别视网膜眼底图像中的较小血管，从而为改善青光眼管理铺平了道路。
