Zhongdi Chu1, Giovanni Gregori2, Philip J Rosenfeld2, Ruikang K Wang3. 1. Department of Bioengineering, University of Washington, Seattle, Washington. 2. Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA. 3. Department of Bioengineering, University of Washington, Seattle, Washington; Department of Ophthalmology, University of Washington, Seattle, Washington. Electronic address: wangrk@uw.edu.
Abstract
PURPOSE: To demonstrate the variation in quantitative choriocapillaris (CC) metrics with various binarization approaches using optical coherence tomography angiography (OCTA). DESIGN: Retrospective, observational, cross-sectional case series. METHODS: Macular OCTA scans, 3- × 3-mm and 6- × 6-mm, were obtained from normal eyes and from eyes with drusen secondary to age-related macular degeneration (AMD). The CC slab was extracted, and the CC flow deficits (FDs) were segmented with 2 previously published algorithms: the fuzzy C-means approach (FCM method) and Phansalkar's local thresholding (Phansalkar method). Four different values for the radius were used in order to investigate the effect on the FD segmentation when using the Phansalkar method. FD density (FDD), mean FD size (MFDS), FD number (FDN), FD area (FDA) and intercapillary distance (ICD) were calculated for comparison. Repeatability was assessed as coefficient of variation (CV), and Pearson's correlation analysis was conducted. RESULTS: Six eyes from 6 subjects with normal eyes and 6 eyes from 6 subjects with drusen secondary to AMD were scanned. The 3- × 3-mm scans resulted in higher repeatability than the 6- × 6-mm scans. For the Phansalkar method, larger values of the radius resulted in higher repeatability. ANOVA tests resulted in significant differences (P < 0.001) among the FCM method and the Phansalkar method with different radius options for all CC metrics and scan sizes investigated. In 3- × 3-mm scans, significant correlation was found between the FCM method and the Phansalkar method for all quantitative CC metrics other than FDN (all P < 0.001; 0.90 < r <0.99). CONCLUSIONS: Quantitative CC analysis with commercially available OCTA is complicated and researchers need to pay close attention to how they conduct such analyses.
PURPOSE: To demonstrate the variation in quantitative choriocapillaris (CC) metrics with various binarization approaches using optical coherence tomography angiography (OCTA). DESIGN: Retrospective, observational, cross-sectional case series. METHODS: Macular OCTA scans, 3- × 3-mm and 6- × 6-mm, were obtained from normal eyes and from eyes with drusen secondary to age-related macular degeneration (AMD). The CC slab was extracted, and the CC flow deficits (FDs) were segmented with 2 previously published algorithms: the fuzzy C-means approach (FCM method) and Phansalkar's local thresholding (Phansalkar method). Four different values for the radius were used in order to investigate the effect on the FD segmentation when using the Phansalkar method. FD density (FDD), mean FD size (MFDS), FD number (FDN), FD area (FDA) and intercapillary distance (ICD) were calculated for comparison. Repeatability was assessed as coefficient of variation (CV), and Pearson's correlation analysis was conducted. RESULTS: Six eyes from 6 subjects with normal eyes and 6 eyes from 6 subjects with drusen secondary to AMD were scanned. The 3- × 3-mm scans resulted in higher repeatability than the 6- × 6-mm scans. For the Phansalkar method, larger values of the radius resulted in higher repeatability. ANOVA tests resulted in significant differences (P < 0.001) among the FCM method and the Phansalkar method with different radius options for all CC metrics and scan sizes investigated. In 3- × 3-mm scans, significant correlation was found between the FCM method and the Phansalkar method for all quantitative CC metrics other than FDN (all P < 0.001; 0.90 < r <0.99). CONCLUSIONS: Quantitative CC analysis with commercially available OCTA is complicated and researchers need to pay close attention to how they conduct such analyses.
Authors: Enrico Borrelli; Eric H Souied; K Bailey Freund; Giuseppe Querques; Alexandra Miere; Orly Gal-Or; Riccardo Sacconi; SriniVas R Sadda; David Sarraf Journal: Retina Date: 2018-10 Impact factor: 4.256
Authors: Yali Jia; Steven T Bailey; Thomas S Hwang; Scott M McClintic; Simon S Gao; Mark E Pennesi; Christina J Flaxel; Andreas K Lauer; David J Wilson; Joachim Hornegger; James G Fujimoto; David Huang Journal: Proc Natl Acad Sci U S A Date: 2015-04-20 Impact factor: 11.205
Authors: Alice Y Kim; Damien C Rodger; Anoush Shahidzadeh; Zhongdi Chu; Nicole Koulisis; Bruce Burkemper; Xuejuan Jiang; Kathryn L Pepple; Ruikang K Wang; Carmen A Puliafito; Narsing A Rao; Amir H Kashani Journal: Am J Ophthalmol Date: 2016-09-02 Impact factor: 5.258
Authors: Yingying Shi; Zhongdi Chu; Liang Wang; Qinqin Zhang; William Feuer; Luis de Sisternes; Mary K Durbin; Giovanni Gregori; Ruikang K Wang; Philip J Rosenfeld Journal: Am J Ophthalmol Date: 2020-07-02 Impact factor: 5.258
Authors: Eric M Moult; Yingying Shi; Qinqin Zhang; Liang Wang; Rahul Mazumder; Siyu Chen; Zhongdi Chu; William Feuer; Nadia K Waheed; Giovanni Gregori; Ruikang K Wang; Philip J Rosenfeld; James G Fujimoto Journal: Biomed Opt Express Date: 2021-07-01 Impact factor: 3.732
Authors: Zhongdi Chu; Qinqin Zhang; Giovanni Gregori; Philip J Rosenfeld; Ruikang K Wang Journal: Am J Ophthalmol Date: 2020-09-04 Impact factor: 5.258
Authors: Yingying Shi; Qinqin Zhang; Hao Zhou; Liang Wang; Zhongdi Chu; Xiaoshuang Jiang; Mengxi Shen; Marie Thulliez; Cancan Lyu; William Feuer; Luis de Sisternes; Mary K Durbin; Giovanni Gregori; Ruikang K Wang; Philip J Rosenfeld Journal: Am J Ophthalmol Date: 2020-12-24 Impact factor: 5.258
Authors: Mengxi Shen; Qinqin Zhang; Jin Yang; Hao Zhou; Zhongdi Chu; Xiao Zhou; William Feuer; Xiaoshuang Jiang; Yingying Shi; Luis de Sisternes; Mary K Durbin; Ruikang K Wang; Giovanni Gregori; Philip J Rosenfeld Journal: Invest Ophthalmol Vis Sci Date: 2021-05-03 Impact factor: 4.799