  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 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.
