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首页> 外文会议>IEEE International Conference on Image Processing >Retinal Vessel Detection in Wide-Field Fluorescein Angiography with Deep Neural Networks: A Novel Training Data Generation Approach
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Retinal Vessel Detection in Wide-Field Fluorescein Angiography with Deep Neural Networks: A Novel Training Data Generation Approach

机译：带有深层神经网络的广域荧光素血管造影术中的视网膜血管检测：一种新型的训练数据生成方法

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Retinal blood vessel detection is a crucial step in automatic retinal image analysis. Recently, deep neural networks have significantly advanced the state of the art for retinal blood vessel detection in color fundus (CF) images. Thus far, similar gains have not been seen in fluorescein angiography (FA) because the FA modality is entirely different from CF and annotated training data has not been available for FA imagery. We address retinal vessel detection in wide-field FA images with generative adversarial networks (GAN) via a novel approach for generating training data. Using a publicly available dataset that contains concurrently acquired pairs of CF and fundus FA images, vessel maps are detected in CF images via a pre-trained neural network and registered with fundus FA images via parametric chamfer matching to a preliminary FA vessel detection map. The co-aligned pairs of vessel maps (detected from CF images) and fundus FA images are used as ground truth labeled data for de novo training of a deep neural network for FA vessel detection. Specifically, we utilize adversarial learning to train a GAN where the generator learns to map FA images to binary vessel maps and the discriminator attempts to distinguish generated vs. ground-truth vessel maps. We highlight several important considerations for the proposed data generation methodology. The proposed method is validated on VAMpIRE dataset that contains high-resolution wide-field FA images and manual annotation of vessel segments. Experimental results demonstrate that the proposed method achieves an estimated ROC AUC of 0.9758.
机译：视网膜血管检测是自动视网膜图像分析中的关键步骤。最近，深层神经网络已大大提高了彩色眼底（CF）图像中视网膜血管检测的技术水平。到目前为止，在荧光素血管造影术（FA）中还没有看到类似的收益，因为FA模态与CF完全不同，并且注释的训练数据还无法用于FA图像。我们通过生成训练数据的新方法解决了在具有生成对抗网络（GAN）的广域FA图像中进行视网膜血管检测的问题。使用包含并发获取的CF和眼底FA图像对的公共数据集，可通过预先训练的神经网络在CF图像中检测血管图，并通过与原始FA血管检测图匹配的参数倒角在眼底FA图像中注册血管图。血管图（从CF图像中检测到）和眼底FA图像对齐的对被用作地面真相标记的数据，用于从头训练深度神经网络以进行FA血管检测。具体来说，我们利用对抗学习来训练GAN，其中生成器学习将FA图像映射到二进制血管图，而鉴别器尝试区分生成的与地面的血管图。我们重点介绍了建议的数据生成方法的几个重要考虑因素。该方法在VAMpIRE数据集上得到了验证，该数据集包含高分辨率的广域FA图像和人工血管段注释。实验结果表明，所提出的方法可实现估计的ROC AUC为0.9758。

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