Abstract
The detection and assessment of leakage in retinal fluorescein angiogram images is important for the management of a wide range of retinal diseases. We have developed a framework that can automatically detect three types of leakage (large focal, punctate focal, and vessel segment leakage) and validated it on images from patients with malarial retinopathy. This framework comprises three steps: vessel segmentation, saliency feature generation and leakage detection. We tested the effectiveness of this framework by applying it to images from 20 patients with large focal leak, 10 patients with punctate focal leak, and 5,846 vessel segments from 10 patients with vessel leakage. The sensitivity in detecting large focal, punctate focal and vessel segment leakage are 95%, 82% and 81%, respectively, when compared to manual annotation by expert human observers. Our framework has the potential to become a powerful new tool for studying malarial retinopathy, and other conditions involving retinal leakage.
Highlights
The detection and assessment of leakage in retinal fluorescein angiogram images is important for the management of a wide range of retinal diseases
Retinal vessel leakage is relevant to cerebral malaria, since the blood-retinal barrier is similar to the blood-brain barrier[6], and leakage from the latter could contribute to the brain swelling commonly seen in paediatric cerebral malaria[7]
Our dataset consisted of retinal Fluorescein angiography (FA) images taken from children with cerebral malaria (CM) admitted to the Paediatric Research Ward, Queen Elizabeth Central Hospital, Blantyre, Malawi
Summary
The detection and assessment of leakage in retinal fluorescein angiogram images is important for the management of a wide range of retinal diseases. We have developed a framework that can automatically detect three types of leakage (large focal, punctate focal, and vessel segment leakage) and validated it on images from patients with malarial retinopathy. This framework comprises three steps: vessel segmentation, saliency feature generation and leakage detection. The sensitivity in detecting large focal, punctate focal and vessel segment leakage are 95%, 82% and 81%, respectively, when compared to manual annotation by expert human observers. Three types of leakage can be observed in malarial retinopathy: large focal, punctate focal, and vessel segment leakage (Fig. 1). Manual annotation can be extremely time consuming, and the performance of the classifier is inherently dependent on the quality of this annotation
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