The Foveal Avascular Zone Image Database (FAZID)

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

doi:10.1117/12.2567580

Cited by 8 publications

(4 citation statements)

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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%

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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%

“…The main contributions of this paper are four-fold: (1) We present the first joint learning framework, named BSDA-Net, for FAZ segmentation and multidisease (e.g. DR, AMD, diabetes and myopia) classification from OCTA images.…”

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

View full text Add to dashboard Cite

show abstract

“…This hampers the validation process as the results obtained by different algorithms cannot be compared. Although a few databases like ROSE [7], OCTAGON [9] and FAZID [21] are available, they suffer from the following shortcomings:…”

Section: Research Gapsmentioning

confidence: 99%

DeepVasNet: Retinal Vasculature and FAZ Segmentation in OCTA with EfficientNet-Enhanced U-Net

Raja,

Gopi,

Francis

et al. 2024

Preprint

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The study and analysis of retinal blood vessels and foveal avascular zone (FAZ) are clinically significant as they help in the early diagnosis of several diseases like diabetic retinopathy, glaucoma, neovasculariza-tion, artery or vein occlusion, and age-related macular degeneration. Recently, optical coherence tomography angiography (OCTA) imaging has been used to visualize the retina due to its non-invasive nature and fast imaging. However, manual retinal vasculature and FAZ segmentation are tedious and time-consuming. In this context, we propose an automated deep-learning-based segmentation method. The traditional U-Net is modified with a pre-trained EfficientNet-B0 in the encoder and a combined spatial, channel attention, squeeze, and excitation mechanism in the decoder. The proposed algorithm is validated on the publicly available OCTA-500 database. Mean Dice coefficients of 88.38, 88.40, 88.48, and 94.55 are obtained for capillary, artery, vein, and FAZ segmenta-tion in the OCTA 6 mm dataset. Whereas mean Dice scores of 90.48, 90.70, 89.59, and 97.98 are reported in the OCTA 3 mm subset for capillary , artery, vein, and FAZ segmentation, respectively. This segmentation performance is superior to other recent methods in the state-of-the-art literature. Overall, the contributions of our work are summarized as DRL-ASOCT follows. The proposed architecture is simple, easy to implement, and requires less memory and time than other networks. Further, we separately segment the retinal vessels (arteries, veins, and capillaries) and achieve improved segmentation accuracy relative to other approaches.

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“…• There are already some OCTA datasets available for RV segmentation [13], [21] or FAZ segmentation [22], but there is no publicly available dataset for MTL and detection of RV, FAZ and RVJ in OCTA. For the first time, we construct a publicly-accessible retinal structure detection dataset of OCTA images, with precise manual annotations of the RV, RVJ and FAZ.…”

Section: Introductionmentioning

confidence: 99%

Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning

Hao

Shen

Zhu

et al. 2022

IEEE Trans. Med. Imaging

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Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Votingbased Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different en face angiograms from various retinal layers, rather than following existing methods that use only a single en face. To validate the superiority of our VAFF-Net, we carry out extensive experiments on three OCTA datasets acquired using different imaging devices, and the results demonstrate that the proposed method performs on the whole better than either the state-of-the-art singlepurpose methods or existing multi-task learning solutions.We also demonstrate that our multi-task learning method generalizes across other imaging modalities, such as color fundus photography, and may potentially be used as a general multi-task learning tool. For the first time in the field of retinal OCTA image analysis, we construct three datasets for multiple structure detection. To facilitate further research, part of these datasets with the source code and evaluation benchmark have been released for public access.

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The Foveal Avascular Zone Image Database (FAZID)

Cited by 8 publications

References 0 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

DeepVasNet: Retinal Vasculature and FAZ Segmentation in OCTA with EfficientNet-Enhanced U-Net

Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning

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