Concordance between SIVA, IVAN, and VAMPIRE Software Tools for Semi-Automated Analysis of Retinal Vessel Caliber

被引:11
|
作者
Mautuit, Thibaud [1 ,2 ]
Cunnac, Pierre [1 ,2 ]
Cheung, Carol Y. [3 ]
Wong, Tien Y. [4 ]
Hogg, Stephen [5 ]
Trucco, Emanuele [5 ]
Daien, Vincent [6 ]
MacGillivray, Thomas J. [7 ]
Labarere, Jose [8 ,9 ]
Chiquet, Christophe [1 ,2 ]
机构
[1] Univ Grenoble Alpes, HP2 Lab, INSERM, U1300, F-38700 La Tronche, France
[2] Univ Hosp Grenoble Alps, Dept Ophthalmol, F-38043 Grenoble 09, France
[3] Chinese Univ Hong Kong, Dept Ophthalmol & Visual Sci, Hong Kong, Peoples R China
[4] Natl Univ Singapore, Yong Loo Ling Sch Med, Singapore Eye Res Inst, Singapore 119077, Singapore
[5] Univ Dundee, Sch Comp, VAMPIRE Project, Dundee DD1 4HN, Scotland
[6] Gui De Chauliac Hosp, Dept Ophthalmol, F-34295 Montpellier, France
[7] Univ Edinburgh, Clin Res Imaging Ctr, VAMPIRE Project, Edinburgh EH8 9YL, Midlothian, Scotland
[8] Grenoble Univ Hosp, Clin Epidemiol Unit, F-38043 Grenoble, France
[9] Univ Grenoble Alpes, CNRS, TIMC IMAG UMR 5525, F-38041 Grenoble, France
关键词
central retinal artery equivalent; central retinal vein equivalent; SIVA software; IVAN software; VAMPIRE software; conversion algorithm; retinal vessel measurements; ATHEROSCLEROSIS RISK; VASCULAR CALIBER; MICROVASCULAR ABNORMALITIES; IMAGE COMPRESSION; DIAMETER;
D O I
10.3390/diagnostics12061317
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
We aimed to compare measurements from three of the most widely used software packages in the literature and to generate conversion algorithms for measurement of the central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE) between SIVA and WAN and between SIVA and VAMPIRE. We analyzed 223 retinal photographs from 133 human participants using both SIVA, VAMPIRE and IVAN independently for computing CRAE and CRVE. Agreement between measurements was assessed using Bland-Altman plots and intra-class correlation coefficients. A conversion algorithm between measurements was carried out using linear regression, and validated using bootstrapping and root-mean-square error. The agreement between VAMPIRE and IVAN was poor to moderate: The mean difference was 20.2 mu m (95% limits of agreement, LOA, -12.2-52.6 mu m) for CRAE and 21.0 mu m (95% LOA, -17.5-59.5 mu m) for CRVE. The agreement between VAMPIRE and SIVA was also poor to moderate: the mean difference was 36.6 mu m (95% LOA, -12.8-60.4 mu m) for CRAE, and 40.3 mu m (95% LOA, 5.6-75.0 mu m) for CRVE. The agreement between IVAN and SIVA was good to excellent: the mean difference was 16.4 mu m (95% LOA, -4.25-37.0 mu m) for CRAE, and 19.3 mu m (95% LOA, 0.09-38.6 mu m) for CRVE. We propose an algorithm converting IVAN and VAMPIRE measurements into SIVA-estimated measurements, which could be used to homogenize sets of vessel measurements obtained with different software packages.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] Chipper: Open-source software for semi-automated segmentation and analysis of birdsong and other natural sounds
    Searfoss, Abigail M.
    Pino, James C.
    Creanza, Nicole
    METHODS IN ECOLOGY AND EVOLUTION, 2020, 11 (04): : 524 - 531
  • [32] BioVision Tracker: A semi-automated image analysis software for spatiotemporal gene expression tracking in Arabidopsis thaliana
    Buckner, Eli
    Madison, Imani
    Melvin, Charles
    Long, Terri
    Sozzani, Rosangela
    Williams, Cranos
    PLANT CELL BIOLOGY, 2020, 160 : 419 - 436
  • [33] BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data
    Nebiolo, K.
    Castro-Santos, T.
    ANIMAL BIOTELEMETRY, 2022, 10 (01)
  • [34] Risk of Normal Tension Glaucoma Progression From Automated Baseline Retinal-Vessel Caliber Analysis: A Prospective Cohort Study
    Lin, Timothy P. H.
    Hui, Herbert Y. H.
    Ling, Annie
    Chan, Poemen P.
    Shen, Ruyue
    Wong, Mandy O. M.
    Chan, Noel C. Y.
    Leung, Dexter Y. L.
    Xu, Dejiang
    LI Lee, Mong
    Hsu, Wynne
    Wong, Tien Yin
    Tham, Clement C.
    Cheung, Carol Y.
    AMERICAN JOURNAL OF OPHTHALMOLOGY, 2023, 247 : 111 - 120
  • [35] Semi-automated, quantitative analysis of retinal ganglion cell morphology in mice selectively expressing yellow fluorescent protein
    Oglesby, Ericka
    Quigley, Harry A.
    Zack, Donald J.
    Cone, Frances E.
    Steinhart, Matthew R.
    Tian, Jing
    Pease, Mary E.
    Kalesnykas, Giedrius
    EXPERIMENTAL EYE RESEARCH, 2012, 96 (01) : 107 - 115
  • [36] Semi-automated procedure of digitalization and study of rock thin section porosity applying optical image analysis tools
    Berrezueta, Edgar
    Jose Dominguez-Cuesta, Maria
    Rodriguez-Rey, Angel
    COMPUTERS & GEOSCIENCES, 2019, 124 : 14 - 26
  • [37] SAFA: Semi-automated footprinting analysis software for high-throughput quantification of nucleic acid footprinting experiments
    Das, R
    Laederach, A
    Pearlman, SM
    Herschlag, D
    Altman, RB
    RNA, 2005, 11 (03) : 344 - 354
  • [38] ASSESSMENT OF JOINT DESTRUCTION AT THE KNEE IN RHEUMATOID ARTHRITIS USING SEMI-AUTOMATED SOFTWARE FOR MAGNETIC RESONANCE IMAGE ANALYSIS
    Oka, H.
    Ohashi, S.
    Kadono, Y.
    Yasui, T.
    Ono, K.
    Isawa, K.
    Yoshimura, N.
    Nishino, J.
    Tanaka, S.
    ANNALS OF THE RHEUMATIC DISEASES, 2014, 73 : 470 - 470
  • [39] Interscan reproducibility of computed tomography derived coronary plaque volume measurements using a semi-automated analysis software
    Iraqi, N.
    Mortensen, M. B.
    Sand, N. P.
    Busk, M.
    Grove, E. L.
    Dey, D.
    Pedersen, K. B.
    Kanstrup, H.
    Madsen, K. T.
    Parner, E.
    Jensen, J. M.
    Noergaard, B. L.
    EUROPEAN HEART JOURNAL, 2024, 45
  • [40] A software package for the semi-automated crystal identification and spectrum analysis in multi-layer DOI PET systems
    Park, Sang Keun
    Kwon, Sun Il
    Lee, Jae Sung
    Lee, Dong Soo
    Hong, Seong Jong
    JOURNAL OF NUCLEAR MEDICINE, 2009, 50