Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy



Zhao, Yitian, MacCormick, Ian, Parry, David, Beare, Nicholas ORCID: 0000-0001-8086-990X, Harding, Simon ORCID: 0000-0003-4676-1158 and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2015) Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy. Scientific Reports, 5.

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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.

Item Type: Article
Additional Information: Cite as; Zhao, Y. et al. Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy. Sci. Rep. 5, 11154; doi: 10.1038/srep11154 (2015).
Uncontrolled Keywords: Medical imaging, Prognostic markers, Retina, Retinal diseases
Depositing User: Symplectic Admin
Date Deposited: 17 Jul 2015 14:25
Last Modified: 23 Jan 2021 17:12
DOI: 10.1038/srep11154
Publisher's Statement : This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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URI: https://livrepository.liverpool.ac.uk/id/eprint/2016820