Automatic blood vessel segmentation from retinal images plays an important role in the diagnosis of many systemic and eye diseases, including retinopathy of prematurity. Current state-of-the-art research in blood vessel segmentation from retinal images is based on convolutional neural networks. The solutions proposed so far are trained and tested on images from a few available retinal blood vessel segmentation datasets, which might limit their performance when given an image with retinopathy of prematurity signs. In this paper, we evaluate the performance of three high-performing convolutional neural networks for retinal blood vessel segmentation in the context of blood vessel segmentation on retinopathy of prematurity retinal images. The main motive behind the study is to test if existing public datasets suffice to develop a high-performing predictor that could assist an ophthalmologist in retinopathy of prematurity diagnosis. To do so, we create a dataset consisting solely of retinopathy of prematurity images with retinal blood vessel annotations manually labeled by two observers, where one is the ophthalmologist experienced in retinopathy of prematurity treatment. Experimental results show that all three solutions have difficulties in detecting the retinal blood vessels of infants due to a lower contrast compared to images from public datasets as demonstrated by a significant drop in classification sensitivity. All three solutions segment alongside retinal also choroidal blood vessels which are not used to diagnose retinopathy of prematurity, but instead represent noise and are confused with retinal blood vessels. By visual and numerical observations, we observe that existing solutions for retinal blood vessel segmentation need improvement toward more detailed datasets or deeper models in order to assist the ophthalmologist in retinopathy of prematurity diagnosis.

翻译：视网膜血管的自动分割在多种全身性及眼部疾病的诊断中具有重要作用，包括早产儿视网膜病变。当前视网膜血管分割领域的最先进研究基于卷积神经网络。现有方案通常利用少数公开视网膜血管分割数据集进行训练和测试，这可能限制其在包含早产儿视网膜病变体征图像上的表现。本文评估了三种高性能卷积神经网络在早产儿视网膜病变图像中分割视网膜血管的性能。本研究的主要动机是检验现有公开数据集是否足以开发出能够辅助眼科医生诊断早产儿视网膜病变的高性能预测模型。为此，我们构建了一个完全由早产儿视网膜病变图像组成的数据集，并由两位观察者（其中一位是具有早产儿视网膜病变治疗经验的眼科医生）手动标注视网膜血管。实验结果表明，与公开数据集图像相比，早产儿视网膜病变图像上的对比度较低，导致三种方案均难以检测婴儿的视网膜血管，分类敏感性显著下降。此外，三种方案均同时分割了视网膜血管和脉络膜血管，后者虽不用于诊断早产儿视网膜病变，却构成干扰噪声，易于与视网膜血管混淆。通过视觉和数值分析，我们发现现有视网膜血管分割方案仍需改进：或需构建更精细的数据集，或需设计更深的模型，才能有效辅助眼科医生诊断早产儿视网膜病变。