Retinal blood vessels, the diagnostic bio-marker of ophthalmologic and diabetic retinopathy, utilise thick and thin vessels for diagnostic and monitoring purposes. The existing deep learning methods attempt to segment the retinal vessels using a unified loss function. However, a difference in spatial features of thick and thin vessels and a biased distribution creates an imbalanced thickness, rendering the unified loss function to be useful only for thick vessels. To address this challenge, a patch-based generative adversarial network-based technique is proposed which iteratively learns both thick and thin vessels in fundoscopic images. It introduces an additional loss function that allows the generator network to learn thin and thick vessels, while the discriminator network assists in segmenting out both vessels as a combined objective function. Compared with state-of-the-art techniques, the proposed model demonstrates the enhanced accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves on STARE, DRIVE, and CHASEDB1 datasets.

中文翻译：

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CPGAN：基于条件补丁的生成对抗网络，用于视网膜血管分割

视网膜血管是眼科和糖尿病性视网膜病的诊断生物标志物，利用厚而薄的血管进行诊断和监测。现有的深度学习方法尝试使用统一的损失函数来分割视网膜血管。但是，厚壁血管和薄壁血管的空间特征的差异以及分布的偏差会导致厚度不平衡，从而使统一的损失函数仅对厚壁血管有用。为了应对这一挑战，提出了一种基于补丁的基于生成对抗网络的技术，该技术可迭代地学习眼底镜图像中的粗细血管。它引入了一个附加的损失函数，该函数允许生成器网络学习薄型和厚型血管，而鉴别器网络则有助于将两个容器进行分段，作为组合的目标函数。