Abstract
Objective: The aim of this research is to present a novel computer-aided decision support tool in analyzing, quantifying, and evaluating the retinal blood vessel structure from fluorescein angiogram (FA) videos. Methods: The proposed method consists of three phases: i) image registration for large motion removal from FA videos, followed by ii) retinal vessel segmentation, and lastly, iii) segmentation-guided video magnification. In the image registration phase, individual frames of the FA video are spatiotemporally aligned using a novel wavelet-based registration approach to compensate for the global camera and patient motion. In the second phase, a capsule-based neural network architecture is employed to perform the segmentation of retinal vessels for the first time in the literature. In the final phase, a segmentation-guided Eulerian video magnification is proposed for magnifying subtle changes in the retinal video produced by blood flow through the retinal vessels. The magnification is applied only to the segmented vessels, as determined by the capsule network. This minimizes the high levels of noise present in FA videos and maximizes useful information, enabling ophthalmologists to more easily identify potential regions of pathology. Results: The collected FA video dataset consists of 1402 frames from 10 normal subjects (prospective study). Experimental results for retinal vessel segmentation show that the capsule-based algorithm outperforms a state-of-the-art CNN (U-Net), obtaining a higher dice coefficient (85.94%) and sensitivity (92.36%) while using just 5% of the network parameters. Qualitative analysis of the FA videos was performed after the final phase by expert ophthalmologists, supporting the claim that artificial intelligence assisted decision support tool can be helpful for providing a better analysis of blood flow dynamics. Conclusions: The authors introduce a novel computational tool, combining a wavelet-based video registration method with a deep learning capsule-based retinal vessel segmentation algorithm and a Eulerian video magnification technique to quantitatively and qualitatively analyze FA videos. To authors' best knowledge, this is the first-ever development of such a computational tool to assist ophthalmologists with analyzing blood flow in FA videos.
Highlights
Fluorescein angiography (FA) is a diagnostic imaging technique that aids ophthalmologists in diagnosing, treating, and monitoring retinal vascular pathology (Almotiri et al, 2018)
Ophthalmologists diagnose abnormalities in the retina and its vasculature using FA images and videos, which are captured by a fundus camera
We reduce the motion in FA frames using our proposed wavelet-based registration algorithm and reduce noise, which is mainly present in the background non-vessel regions, using the masks obtained from our proposed capsule-based segmentation algorithm
Summary
Fluorescein angiography (FA) is a diagnostic imaging technique that aids ophthalmologists in diagnosing, treating, and monitoring retinal vascular pathology (Almotiri et al, 2018) In this procedure, a fluorescent dye is injected into the patient’s bloodstream to highlight the blood vessels in the retina. Due to the limitation in image capture angle, smaller image regions are frequently considered instead of whole fundus images; a sufficient number of small image regions are required to obtain full information of eye diseases (Guo et al, 2017) Due to these above difficulties in the alignment process, the problem of retinal image registration has been a major area of research
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