title: Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy.
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.
authors.affiliation: 1] School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China [2] Department of Eye and Vision Science, University of Liverpool, Liverpool, UK.
authors.forename: Yitian
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: 1] Department of Eye and Vision Science, University of Liverpool, Liverpool, UK [2] Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi.
authors.forename: Ian J C
authors.initials: IJ
authors.lastname: MacCormick
authors.affiliation: St. Pauls Eye Unit, Royal Liverpool University Hospital, Liverpool, UK.
authors.forename: David G
authors.initials: DG
authors.lastname: Parry
authors.affiliation: 1] Department of Eye and Vision Science, University of Liverpool, Liverpool, UK [2] St. Pauls Eye Unit, Royal Liverpool University Hospital, Liverpool, UK.
authors.forename: Nicholas A V
authors.initials: NA
authors.lastname: Beare
authors.affiliation: 1] Department of Eye and Vision Science, University of Liverpool, Liverpool, UK [2] St. Pauls Eye Unit, Royal Liverpool University Hospital, Liverpool, UK.
authors.forename: Simon P
authors.initials: SP
authors.lastname: Harding
authors.affiliation: 1] Department of Eye and Vision Science, University of Liverpool, Liverpool, UK [2] St. Pauls Eye Unit, Royal Liverpool University Hospital, Liverpool, UK.
authors.forename: Yalin
authors.initials: Y
authors.lastname: Zheng
mesh_terms: D001921:Brain / Q000098:blood supply* / Q000000981:diagnostic imaging; D002533:Cerebral Angiography / Q000379:methods*; D002561:Cerebrovascular Disorders / Q000175:diagnosis* / Q000000981:diagnostic imaging; D002648:Child; D019793:Fluorescein; D005451:Fluorescein Angiography; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016779:Malaria, Cerebral / Q000473:pathology*; D008295:Malawi; D012160:Retina / Q000473:pathology; D012171:Retinal Vessels / Q000000981:diagnostic imaging*
chemical_list: D019793:Fluorescein