Literature DB >> 19324866

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

Christopher G Owen1, Alicja R Rudnicka, Robert Mullen, Sarah A Barman, Dorothy Monekosso, Peter H Whincup, Jeffrey Ng, Carl Paterson.   

Abstract

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.

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Year:  2009        PMID: 19324866     DOI: 10.1167/iovs.08-3018

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  28 in total

1.  Multi-level deep supervised networks for retinal vessel segmentation.

Authors:  Juan Mo; Lei Zhang
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-06-02       Impact factor: 2.924

2.  Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform.

Authors:  Masoud Elhami Asl; Navid Alemi Koohbanani; Alejandro F Frangi; Ali Gooya
Journal:  J Med Imaging (Bellingham)       Date:  2017-09-12

3.  An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images.

Authors:  Jyotiprava Dash; Nilamani Bhoi
Journal:  J Digit Imaging       Date:  2018-12       Impact factor: 4.056

4.  Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; the Child Heart and Health Study in England (CHASE).

Authors:  Christopher G Owen; Alicja R Rudnicka; Claire M Nightingale; Robert Mullen; Sarah A Barman; Naveed Sattar; Derek G Cook; Peter H Whincup
Journal:  Arterioscler Thromb Vasc Biol       Date:  2011-06-09       Impact factor: 8.311

5.  U-shaped Retinal Vessel Segmentation Based on Adaptive Aggregation of Feature Information.

Authors:  Liming Liang; Jun Feng; Longsong Zhou; Jiang Yin; Xiaoqi Sheng
Journal:  Interdiscip Sci       Date:  2022-04-29       Impact factor: 2.233

6.  OTNet: A CNN Method Based on Hierarchical Attention Maps for Grading Arteriosclerosis of Fundus Images with Small Samples.

Authors:  Hang Bai; Li Gao; Xiongwen Quan; Han Zhang; Shuo Gao; Chuanze Kang; Jiaqiang Qi
Journal:  Interdiscip Sci       Date:  2021-09-18       Impact factor: 2.233

Review 7.  A Detailed Systematic Review on Retinal Image Segmentation Methods.

Authors:  Nihar Ranjan Panda; Ajit Kumar Sahoo
Journal:  J Digit Imaging       Date:  2022-05-04       Impact factor: 4.903

8.  Measurement of retinal vascular tortuosity and its application to retinal pathologies.

Authors:  Geoff Dougherty; Michael J Johnson; Matthew D Wiers
Journal:  Med Biol Eng Comput       Date:  2009-12-11       Impact factor: 2.602

Review 9.  Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification.

Authors:  Muhammad Moazam Fraz; Alicja R Rudnicka; Christopher G Owen; Sarah A Barman
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-12-24       Impact factor: 2.924

10.  "Keep it simple, scholar": an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging.

Authors:  Weilin Fu; Katharina Breininger; Roman Schaffert; Zhaoya Pan; Andreas Maier
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-04-30       Impact factor: 2.924

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