Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program.

By Christopher G. Owen, Alicja R. Rudnicka, R. Mullen, Sarah Ann Barman, Dorothy Ndedi Monekosso, P. Whincup, Jeffrey Ng, C. Paterson. Investigative Ophthalmology and Visual Science, 2009

447 citations recorded by the source corpus.

PURPOSE To examine the agreement of a novel computer program measuring retinal vessel tortuosity with subjective assessment of tortuosity in school-aged children. METHODS Cross-sectional study of 387 retinal vessels (193 arterioles, 194 veins) from 28 eyes of 14 children (aged 10 years). Retinal digital images were analyzed using the Computer Assisted Image Analysis of the Retina (CAIAR) program, including 14 measures of tortuosity. Vessels were graded (from 0 = none; to 5 = tortuous) independently by two observers. Interobserver agreement was assessed by using kappa statistics. Agreement with all 14 objective measures was assessed with correlation/regression analyses. Intersession repeatability (comparing morning and afternoon sessions) of tortuosity indices was calculated. RESULTS Interobserver agreement of vessel tortuosity within one grade was high (kappa = 0.97), with total agreement in 56% of grades and 42% differing by +/-1 grade. Tortuosity indices based on subdivided chord length methods showed strong log-linear associations with agreed subjective grades (typically r > 0.6; P < 0.001). An approach that averages the distance from the vessel to chord length along the length of the vessel showed best agreement (r = 0.8; P < 0.0001). Tortuosity measures based on curvature performed less well. Intersession repeatability of the vessel to chord technique was good, differing by values equivalent to <1 in subjective grade. CONCLUSIONS Tortuosity indices based on changes in subdivided chord lengths showed optimal agreement with subjective assessment. The relation of these indices to ethnicity and cardiovascular risk factors in childhood should be examined further, as these indices may be a useful indicator of early vascular function.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c7cfe0b77fd35edc8a87b508fc988b8270d35ba1

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Citations in the corpus

Trainable COSFIRE filters for vessel delineation with application to retinal images (2015, unknown): “, 2000) and CHASE_DB1 (Owen et al., 2009).”
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks (2019, background): “We also conducted the comparison experiments on CHASE-DB1 and STARE, which do not have officiallyspecified training and test sets as well as the FoV masks.”
Segmenting Retinal Blood Vessels With Deep Neural Networks (2016, unknown): “The CHASE database (CHASE_DB1 in [11]4) is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England and comprises 28 images with a resolution of 1280 960 pixels.”
DUNet: A deformable network for retinal vessel segmentation (2018, methodology): “The CHASE dataset contains 28 images corresponding to two per patient for 14 children in the program Child Hear And Health Study in England [36].”
Blood vessel segmentation methodologies in retinal images - A survey (2012, background): “tribute to challenges in image processing when retinal images from school children are considered [133].”
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016, methodology): “In this work, four established publicly available color retinal fundus datasets DRIVE [28], STARE [7], CHASE_DB1 [30] and the High-Resolution Fundus dataset HRF [31] and our two newly public IOSTAR [24] and RC-SLO [25] (RetinaCheck Scanning Laser Ophthalmoscopy) datasets are used for evaluating the ”
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, methodology): “Four public datasets DRIVE [23], STARE [38], CHASE_ DB1 [39] and HRF [40] are used to evaluate the proposed”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, methodology): “Three public datasets DRIVE [20], STARE [38] and CHASE_DB1 [39] are used for evaluation.”
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012, background): “The morphology of retinal vessels (particularly arterioles) has also been linked to cardiovascular risk factors both in early and adult life [3].”
Computer Methods and Programs in Biomedicine (unknown): “[68] 40 CFP ARIA [137] 143 CFP CHASE [138] 28 CFP HRF [139] 45 CFP IMAGERET [140] 219 CFP MESSIDOR 1200 CFP ( http://messidor.crihan.fr ) REVIEW [141] 16 CFP ROC [142] 100 CFP VICAVR 58 CFP ( http://www.varpa.es ) VAMPIRE 8 FA ( http://vampire.computing . dundee.ac.uk) CASDQEF [143] Coronary 48 CTA…”