Comparison of foveal avascular zone in diabetic retinopathy, high myopia, and normal fundus images

Balaji, J. Jothi; Agarwal, Amit; Raman, Rajiv; Lakshminarayanan, Vasudevan

doi:10.1117/12.2544817

Cited by 11 publications

(5 citation statements)

References 56 publications

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“…Even in FAZID, samples misclassified in the diabetic category are mainly diabetic non-retinopathy samples. The classification performance on myopia in the FAZID dataset is relatively inferior (79.68%), which partially agrees with existing evidence that FAZ features are relatively indistinguishable in low-moderate myopia [1,4].…”

Section: Experiments and Resultssupporting

confidence: 86%

“…Existing evidence suggests that the morphology and contour irregularity of FAZ are highly relevant to various ocular pathologies such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and so on [17,23,31]. For instance, patients with high myopia typically have reduced FAZ areas, whereas macular ischemia caused by diabetes have enlarged FAZ areas [4,21]. As such, precise FAZ segmentation is of great clinical significance.…”

Section: Introductionmentioning

confidence: 99%

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BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

Lin¹,

Wang²,

Wu³

et al. 2021

Preprint

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Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal layers. Clinical researches suggest that the morphology and contour irregularity of FAZ are important biomarkers of various ocular pathologies. Therefore, precise segmentation of FAZ has great clinical interest. Also, there is no existing research reporting that FAZ features can improve the performance of deep diagnostic classification networks. In this paper, we propose a novel multi-level boundary shape and distance aware joint learning framework, named BSDA-Net, for FAZ segmentation and diagnostic classification from OCTA images. Two auxiliary branches, namely boundary heatmap regression and signed distance map reconstruction branches, are constructed in addition to the segmentation branch to improve the segmentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier. Through extensive experiments, the proposed BSDA-Net is found to yield state-of-the-art segmentation and classification results on the OCTA-500, OCTAGON, and FAZID datasets.

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Section: Experiments and Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

Lin¹,

Wang²,

Wu³

et al. 2021

Preprint

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show abstract

“…For example, the Cirrus 5000 Angioplex OCT device (Carl Zeiss Meditec Inc., Dublin, CA) and AngioVue (Optovue, Inc., Fremont, CA) segment the FAZ boundary. 1 , 12 A better segmentation method than that provided by the Cirrus 5000 Angioplex has been proposed by Agarwal et al 15 An extensive list of parameters defining the FAZ dimensions were calculated, The results of this study were compared with the ground truth and a segmentation accuracy of 98.01% was achieved 15 Additionally, most inbuilt automated methods provide FAZ dimensions of only the superficial layer and not the deep layer. To the best of our knowledge, there are no software tools available to segment the deep layer FAZ.…”

Section: Introductionmentioning

confidence: 92%

“…Several studies have reported FAZ data based on optical coherence tomography angiography (OCTA) images. 5 , 7 , 9 , 12 There are many ways in which the FAZ can be segmented in OCTA generated images. 13 , 14 Clinicians can use manual or system-generated markings.…”

Section: Introductionmentioning

confidence: 99%

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

Viekash¹,

Balaji²,

Lakshminarayanan³

2021

OPTH

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Introduction Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ’s deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). Methods Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. Results The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0–66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. Conclusion A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device’s inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.

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“…The CMT was calculated by an algorithm inbuilt in the device. The foveal microvascular dimensions were calculated using a customdeveloped MATLAB program [22] and this algorithm has been used to study the FAZ dimensions in normal, diabetic and myopic eyes [23]. The macular cube utilized a 6 mm x 6 mm (vertical x horizontal) window axial scans in the macular region.…”

Section: Central Macular Thickness and Foveal Microvascular Dimensionsmentioning

confidence: 99%

Does a Relationship Exist Between Posterior Vitreous Detachment, Macular Thickness and Foveal Avascular Zone Dimensions in Myopic Eyes?

Balaji

Agarwal

Raman

et al. 2021

Preprint

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Background: The posterior vitreous detachment (PVD) is a separation of the posterior vitreous cortex from the internal limiting membrane of the retina. The PVD induces several potentially serious pathologic events at the vitreoretinal interface. The aim of the study is to determine if relationships exist between PVD, macular thickness (MT) and the foveal avascular zone (FAZ) in myopic eyes. Methods: This is a retrospective case study of 63 myopic subjects who underwent comprehensive eye examination including the optical coherence tomography angiography (OCTA) between January 1 and Jun 30, 2019. The spherical equivalent (SE) was calculated using the manifest refraction. The myopia grouping was based on a severity scale, namely mild, moderate, high and very high using standard refractive error classifications. The PVD classification was based on optical coherence tomography (OCT) images. The status of the PVD and MT were evaluated with Macular Cube 200 × 200 images and the FAZ was imaged with an OCTA, Angioplex. The MT and FAZ dimensions were calculated using a custom algorithm. Results: A total of 114 myopic eyes subjects had median (range) age of 26.00 (22.00 - 28.00) years. Of this cohort, females comprised 62.3 % of the dataset, and the right eyes were 50.00 %. In this population, 10 eyes had no PVD in any quadrant, and 73 eyes had incomplete PVD in all four quadrants. The inferior quadrant had the maximum number of PVD cases and the nasal quadrant had the least number of cases. High myopic eyes exhibited significantly increased low foveal volume (p = 0.000). The inferior part of para- and perimacular area showed a significant thinning in very high myopic eyes (p = <0.05). A statistically significant alteration of FAZ circularity index found in very high myopic eyes (p = 0.002). Conclusion: In high and very high myopic eyes, an increasing trend of partial PVD is seen. The most commonly involved location was the inferior quadrant. A significant alteration in foveal volume and circularity index of the FAZ is seen in high and very high myopic eyes.

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Comparison of foveal avascular zone in diabetic retinopathy, high myopia, and normal fundus images

Cited by 11 publications

References 56 publications

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

Does a Relationship Exist Between Posterior Vitreous Detachment, Macular Thickness and Foveal Avascular Zone Dimensions in Myopic Eyes?

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