A Novel Deep Learning Pipeline for Retinal Vessel Detection In Fluorescein Angiography.

ABSTRACT: 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

SUBMITTER: Ding L 

PROVIDER: S-EPMC7648732 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature