Quantitative Fluorescein Angiography

Walsh, Alexander C.; Updike, P.; Sadda, Srinivas R

doi:10.1016/b978-0-323-02598-0.50058-6

Cited by 3 publications

(15 citation statements)

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“… 18 Selection of the frame based on degree of intravascular fluorescence (as in the current study) is considered better compared to frame selected on absolute time as the latter may be influenced by factors such as rapidity of injection and vascular anatomy. 12 Another study of intravitreal bevacizumab reported a significant reduction in GLD on FA at 3 months using incorporated measuring software (ImageNet 2000; Topcon, Tokyo, Japan). 16 None of these studies analyzed segmented images.…”

Section: Discussionmentioning

confidence: 99%

“…Compared to nonsegmented image analysis, a digital image processing strategy through FA image feature segmentation provides a better opportunity. 12 The purpose of our study was to identify quantitative parameters for CNV lesions on segmented FA images and to correlate these with treatment outcome. In this study, pretreatment and posttreatment segmented FA images were analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative analysis of segmented fluorescein angiography images for the follow-up of choroidal neovascular membrane

Ghosh

Haldar

Ravindran

et al. 2015

Middle East Afr J Ophthalmol

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Purpose:The aim of this study was to evaluate choroidal neovascular (CNV) lesions with fluorescein angiography (FA) and to identify quantitative parameters and correlate these parameters to treatment outcomes.Subjects and Methods:This institution based cross-sectional study evaluated 30 eyes with active sub-foveal predominantly classic CNV treated with bevacizumab. Pre- and post-injection segmented FA images were analyzed. Lesion area and CNV lesion were manually delineated. Outcome measure was the change 1-month after each injection in different intensity values (0–255 divided in eight regions A [lowest intensity] to H [highest intensity] on a linear scale) in lesion area, perimeter, greatest linear dimension (GLD), area, visual acuity (VA) and central macular thickness (CMT).Results:At month 3, statistically significant changes from baseline occurred in VA, CMT, lesion area, GLD and perimeter (P < 0.05 all comparisons). Change in CMT from baseline to 3 months postinjection was correlated with change in VA (P = 0.009, r = 0.469) and intensity regions B (P = 0.001, r = −0.565), D (P = 0.001, r = 0.560), E (P = 0.035, r = 0.386). At month 3, change in intensity values 0–63 (A + B) was negatively correlated with CMT (P = 0.001, r = −0.575) and lesion area (P = 0.019, r = −0.427); change in intensity values 64–223 (C-G) was positively correlated with CMT (P = 0.000, r = 0.636) and lesion area (P = 0.002, r = 0.551).Conclusions:Decrease in area, GLD, perimeter and area with intensity ≥ 64 on segmented FA were associated with a favorable outcome of treatment. These parameters may be useful adjuncts to existing evaluation techniques during follow-up of CNV.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of segmented fluorescein angiography images for the follow-up of choroidal neovascular membrane

Ghosh

Haldar

Ravindran

et al. 2015

Middle East Afr J Ophthalmol

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“…Numerous publications reported different methods for retinal lesions size quantification on black-white photos recorded during FA, RF examination, scanning laser ophthalmoscopy, etc. Some authors measured in millimeters or pixels the retinal lesions greatest linear dimension GLD -GLD [mm] or GLD[pix] [5,14,19,[22][23][24][25]. The retinal lesions mouse-driven delimited area (mdrdA) was quantified in total number of pixels -A[tpix] [7,[26][27][28][29][30] in MPS or DA units after defining the optic nerve head (ONH) area as a standard MPS or DA unit -A[MPS] or A[DA] [5,14,20,22,23,25,[31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

“…Some authors measured in millimeters or pixels the retinal lesions greatest linear dimension GLD -GLD [mm] or GLD[pix] [5,14,19,[22][23][24][25]. The retinal lesions mouse-driven delimited area (mdrdA) was quantified in total number of pixels -A[tpix] [7,[26][27][28][29][30] in MPS or DA units after defining the optic nerve head (ONH) area as a standard MPS or DA unit -A[MPS] or A[DA] [5,14,20,22,23,25,[31][32][33][34][35][36][37][38][39]. Other methods expressed the retinal lesions area in mm 2 A[mm 2 ] after the approximation of ONH diameter as 1.5mm or 1.8mm, consequently, the ONH area being 1.77mm 2 or 2.54mm 2 .…”

Section: Introductionmentioning

confidence: 99%

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2022

BJSTR

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SITE-App algorithm variant 2007 was first reported at ARVO Meeting, Fort Lauderdale, 2008, as an audio-visual poster. Results of measurements on fluorescein angiography and red free images realized by using SITE-App software were presented as posters at ARVO Meeting, Fort Lauderdale, 2009 and 2010, SITE-App variant 2021 was published on the website https://www.eyestorywithnarcisa.com, SITE-App variant 2022 is finished and soon to be released to publication. PurposeTo design an algorithm for quantitative assessment of retinal lesions size in fluorescein angiography (FA).Methods: Black-white fundus photos were recorded during FA from patients with age related macular degeneration, before and after Bevacizumab intravitreal injections. Area of "white lesions" was quantified in white pixels inside standardized retinal areas of interest, by using SITE-App algorithm. Method reproducibility was assessed by comparing two independent readers measurements. Method sensitivity was evaluated by comparing SITE-App results with the ones of other published quantitative methods: greatest linear dimension in millimeters -GLD [mm] or pixels -GLD[pix], mouse-delimited lesions area in MPS units -A[MPS], mm2 -A[mm2], total pixels -A[tpix] or white pixels A[wpix]. Correlation between methods was analyzed by calculation of correlation factor.Results: SITE-App is reproducible because differences between two independent readers measurements were not statistically significant (p>0.05). The reproducibility was found very good for SITE-App (p=0.49) and A[wpix] (p=0.22). The reproducibility was lower, but still good for A[MPS] and A[mm2] (p=0.13). A[tpix] (p=0.01), GLD[pix] (p=0.0003) and GLD[mm] (p=0.0005) were found not reproducible. The highest sensitivity was observed for SITE-App (p1=0.002, p2=0.021) and A[wpix] (p1=0.007, p2=0.02), that also showed high correlation (r1=0,96, r2=0,94). A[MPS] and A[mm2] offered very dispersed and unsure results (R1=1.54±26.1, R2=3.51±17.3). No statistical significance of data correction for incidence angle variations of images capture was found. Conclusion:Quantification of retinal lesions in white (or black) pixels inside of interest standardized areas (SITE-App algorithm) is reproducible, sensitive, easy, fast and low cost, very useful for longitudinal clinical studies.

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2-D registration and 3-D shape inference of the retinal fundus from fluorescein images

Choe

Medioni

Cohen

et al. 2008

Medical Image Analysis

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This study presents methods to 2-D registration of retinal image sequences and 3-D shape inference from fluorescein images. The Y-feature is a robust geometric entity that is largely invariant across modalities as well as across the temporal grey level variations induced by the propagation of the dye in the vessels. We first present a Y-feature extraction method that finds a set of Y-feature candidates using local image gradient information. A gradient-based approach is then used to align an articulated model of the Y-feature to the candidates more accurately while optimizing a cost function. Using mutual information, fitted Y-features are subsequently matched across images, including colors and fluorescein angiographic frames, for registration. To reconstruct the retinal fundus in 3-D, the extracted Y-features are used to estimate the epipolar geometry with a plane-and-parallax approach. The proposed solution provides a robust estimation of the fundamental matrix suitable for plane-like surfaces, such as the retinal fundus. The mutual information criterion is used to accurately estimate the dense disparity map, while the Y-features are used to estimate the bounds of the range space. Our experimental results validate the proposed method on a set of difficult fluorescein image pairs.

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Quantitative Fluorescein Angiography

Cited by 3 publications

References 233 publications

Quantitative analysis of segmented fluorescein angiography images for the follow-up of choroidal neovascular membrane

Quantitative analysis of segmented fluorescein angiography images for the follow-up of choroidal neovascular membrane

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2-D registration and 3-D shape inference of the retinal fundus from fluorescein images

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