深度学习视网膜血管分割方法：在早产儿视网膜病变图像上的评估
Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity
AI总结：
本研究评估三种卷积神经网络在早产儿视网膜病变图像上的血管分割性能，发现现有模型因对比度低而灵敏度下降，且易混淆脉络膜血管，需更详细数据集或更深模型以辅助诊断。
摘要：
从视网膜图像中自动分割血管在许多全身性和眼部疾病（包括早产儿视网膜病变）的诊断中起着重要作用。当前基于视网膜图像进行血管分割的最先进研究基于卷积神经网络。迄今为止提出的解决方案是在少数可用的视网膜血管分割数据集图像上训练和测试的，这可能会限制其在含有早产儿视网膜病变迹象的图像上的性能。在本文中，我们评估了三种高性能卷积神经网络在早产儿视网膜病变视网膜图像血管分割背景下的性能。该研究的主要动机是测试现有的公共数据集是否足以开发一个高性能的预测器，以协助眼科医生诊断早产儿视网膜病变。为此，我们创建了一个仅包含早产儿视网膜病变图像的数据集，其视网膜血管注释由两名观察者手动标记，其中一名是经验丰富的早产儿视网膜病变治疗眼科医生。实验结果表明，由于与公共数据集图像相比对比度较低，所有三种解决方案在检测婴儿视网膜血管方面都存在困难，这表现为分类灵敏度的显著下降。所有三种解决方案都分割了视网膜以及脉络膜血管，这些血管不用于诊断早产儿视网膜病变，而是代表噪声，并与视网膜血管混淆。通过视觉和数值观察，我们注意到现有的视网膜血管分割解决方案需要改进，朝向更详细的数据集或更深的模型，以便协助眼科医生诊断早产儿视网膜病变。
英文摘要：
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.
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