While recent advances in deep learning have significantly advanced the state of the art for vessel detection in color fundus (CF) images, the success for detecting vessels in fluorescein angiography (FA) has been stymied due to the lack of labeled ground truth datasets. We propose a novel pipeline to detect retinal vessels in FA images using deep neural networks (DNNs) that reduces the effort required for generating labeled ground truth data by combining two key components: cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured CF and fundus FA images. Binary vessels maps are first detected from CF images with a pre-trained neural network and then are geometrically registered with and transferred to FA images via robust parametric chamfer alignment to a preliminary FA vessel detection obtained with an unsupervised technique. Using the transferred vessels as initial ground truth labels for deep learning, the human-in-the-loop approach progressively improves the quality of the ground truth labeling by iterating between deep-learning and labeling. The approach significantly reduces manual labeling effort while increasing engagement. We highlight several important considerations for the proposed methodology and validate the performance on three datasets. Experimental results demonstrate that the proposed pipeline significantly reduces the annotation effort and the resulting deep learning methods outperform prior existing FA vessel detection methods by a significant margin. A new public dataset, RECOVERY-FA19, is introduced that includes high-resolution ultra-widefield images and accurately labeled ground truth binary vessel maps.

中文翻译：

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用于荧光血管造影中视网膜血管检测的新型深度学习管道。


虽然深度学习的最新进展显着提高了彩色眼底 (CF) 图像中血管检测的技术水平，但由于缺乏标记的地面真实数据集，荧光素血管造影 (FA) 中血管检测的成功受到阻碍。我们提出了一种使用深度神经网络 (DNN) 检测 FA 图像中视网膜血管的新颖流程，通过结合跨模态传输和人机循环学习这两个关键组件，减少生成标记地面实况数据所需的工作量。跨模态传输利用同时捕获的 CF 和眼底 FA 图像。首先使用预先训练的神经网络从 CF 图像中检测二元血管图，然后通过稳健的参数倒角对齐与 FA 图像进行几何配准并传输到 FA 图像，以通过无监督技术获得初步 FA 血管检测。使用转移的血管作为深度学习的初始地面实况标签，人机循环方法通过深度学习和标记之间的迭代逐步提高地面实况标签的质量。该方法显着减少了手动标记工作，同时提高了参与度。我们强调了所提出的方法的几个重要考虑因素，并验证了三个数据集的性能。实验结果表明，所提出的流程显着减少了注释工作，并且由此产生的深度学习方法明显优于现有的 FA 血管检测方法。引入了一个新的公共数据集 RECOVERY-FA19，其中包括高分辨率超宽场图像和准确标记的地面实况二元血管图。