Abstract
The detection and assessment of intravascular filling defects is important, because they may represent a process central to cerebral malaria pathogenesis: neurovascular sequestration. We have developed and validated a framework that can automatically detect intravascular filling defects in fluorescein angiogram images. It first employs a state-of-the-art segmentation approach to extract the vessels from images and then divide them into individual segments by geometrical analysis. A feature vector based on the intensity and shape of saliency maps is generated to represent the level of abnormality of each vessel segment. An AdaBoost classifier with weighted cost coefficient is trained to classify the vessel segments into normal and abnormal categories. To demonstrate its effectiveness, we apply this framework to 6,358 vessel segments in images from 10 patients with malarial retinopathy. The test sensitivity, specificity, accuracy, and area under curve (AUC) are 74.7%, 73.5%, 74.1% and 74.2% respectively when compared to the reference standard of human expert manual annotations. This performance is comparable to the agreement that we find between human observers of intravascular filling defects. Our method will be a powerful new tool for studying malarial retinopathy.
Highlights
Cerebral malaria (CM) is a major cause of death and disability, especially in children in sub-Saharan Africa
Capillary non-perfusion[13], to the best of our knowledge automated quantification of Intravascular filling defects (IVFD) has not yet been attempted. We address this by presenting a framework for automated detection of IVFD, with the aim of quantifying an under-researched retinal feature that has plausible links to the fundamental disease process involved in cerebral malaria
Our automated framework was evaluated against a dataset containing 6,358 vessel segments (3,033 abnormal segments) from 10 retinal fluorescein angiogram (FA) images with a size of 3008 × 1 960 pixels
Summary
Cerebral malaria (CM) is a major cause of death and disability, especially in children in sub-Saharan Africa. Capillary non-perfusion[13], to the best of our knowledge automated quantification of IVFD has not yet been attempted We address this by presenting a framework for automated detection of IVFD, with the aim of quantifying an under-researched retinal feature that has plausible links to the fundamental disease process involved in cerebral malaria. In their work[19], a density analysis method is first used to detect the vessels, connectivity analysis is performed to establish vessel trees, and arterioles are separated from venules by analysing vessel colour and width so as to assess arteriolar narrowing. This method had a sensitivity of about 75%.
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