Automated quantification of choroidal neovascularization on Optical Coherence Tomography Angiography images

Taibouni, Kawther; Chenoune, Yasmina; Mière, Alexandra; Colantuono, Donato; Souied, Eric H.; Petit, Éric

doi:10.1016/j.compbiomed.2019.103450

Cited by 8 publications

(13 citation statements)

References 19 publications

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“…A larger sample size would assist in identifying the association of various CNVM patterns on OCT-A and their prognostic value using an AI-based approach. Quantitative measures, including total vascular area (TVA), the total area (TA) and the vascular density (VD), require add-on algorithms or time-consuming manual measurements [ 32 , 33 ]. Manual measurements, such as those employed by Jia et al, involved quantification of blood flow within a CNV by multiplying the number of pixels and the pixel size after using the split-spectrum amplitude-decorrelation angiography (SSADA) algorithm to improve the signal-to-noise ratio [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

“…Manual measurements, such as those employed by Jia et al, involved quantification of blood flow within a CNV by multiplying the number of pixels and the pixel size after using the split-spectrum amplitude-decorrelation angiography (SSADA) algorithm to improve the signal-to-noise ratio [ 33 ]. Whereas Taibouni et al developed an automated segmentation algorithm that reduced noise and enhanced vessels by Frangi filtering [ 32 ]. Currently, an embedded, quantitative algorithm does not exist in OCT-A devices, which makes it difficult to perform these measurements in a routine clinical practice.…”

Section: Discussionmentioning

confidence: 99%

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Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Sodhi

Pereira

Oakley³

et al. 2022

PLoS ONE

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Purpose To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. Methods In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. Results Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). Conclusions In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Sodhi

Pereira

Oakley³

et al. 2022

PLoS ONE

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“…Among these approaches, Hessian-based filtering has been shown to result in the best quality of final image [ 25 ]. Other works have employed a Frangi filter to improve visibility of vessels in OCTA images of the retina [ 49 ], choroid [ 67 ], and skin [ 41 ]. Despite its widespread use, the Frangi filter has also been criticized in some studies for introducing errors in the vessel architecture, either by missing vessels rendered in the image with low SNR or generating spurious vessels depending on the structure of the background noise [ 68 , 69 ].…”

Section: Discussionmentioning

confidence: 99%

“…Several recent studies in retinal OCTA have evaluated the performance of various traditional segmentation algorithms and found significant variability between them [ 17 , 28 – 30 ]. Of these, the fuzzy means algorithm has shown promise for imaging in the choroid and retina [ 67 , 72 ]. Combined with the results presented here in skin, the fuzzy means algorithm is beginning to stand out as a good option for standardizing the processing workflow.…”

Section: Discussionmentioning

confidence: 99%

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

et al. 2021

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Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

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“…In the case of OCTA image segmentation, the majority of the analyzed studies used pixel intensity as a way to group together objects, using common methods such as k-means clustering [63][64][65], or other clustering algorithms such as fuzzy c-means clustering [66] and a modified CLIQUE clustering technique [67]. An interesting study that used local features for clustering and not pixel intensity is a method by Engberg et al [68] which was based on building a dictionary using pre-annotated data and then processing the unseen images by computing the similarity/dissimilarity.…”

Section: Clusteringmentioning

confidence: 99%

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

et al. 2021

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Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning.

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Automated quantification of choroidal neovascularization on Optical Coherence Tomography Angiography images

Cited by 8 publications

References 19 publications

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

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