Modeling, Localization, and Segmentation of the Foveal Avascular Zone on Retinal OCT-Angiography Images

Carmona, Enrique J.; Díaz, Macarena; Novo, Jorge

doi:10.1109/access.2020.3017440

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Although a large body of literature is available regarding different image processing techniques for automatic delineation of FAZ area in various retinal imaging modalities 17 – 26 , studies focusing on FAZ segmentation in OCTA were usually conducted on healthy subjects (Supplementary Table S1 ). In addition, few studies assessing the accuracy of FAZ delineation in OCTA images of diabetic eye have failed to exhibit a high correlation (Intersection over Union: 0.70 21 and 0.82 20 ), due to high incidence of signal noise and artifacts in OCTA imaging of diabetic patients 16 .…”

Section: Discussionmentioning

confidence: 99%

Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

Mirshahi

Anvari

Riazi-Esfahani

et al. 2021

Sci Rep

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The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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Section: Discussionmentioning

confidence: 99%

Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

Mirshahi

Anvari

Riazi-Esfahani

et al. 2021

Sci Rep

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“…Other authors, such as Díaz et al [ 30 ], also studied the automated segmentation of the FAZ in healthy and diabetic patients and their comparison with manual segmentation, obtaining poor results in the FAZ that were irregular and did not follow the acircularity, especially in the 3 × 3

OCTA protocol. Although many studies are available regarding different image processing techniques for automatic segmentation of the FAZ in various retinal imaging modalities [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], studies focusing on automatic FAZ segmentation in OCTA were usually conducted on healthy subjects. In addition, a few studies assessing the accuracy of FAZ delineation in OCTA images of diabetic eyes have obtained poor results due to the high incidence of signal noise and artefacts in OCTA imaging of diabetic patients [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

“…Although many studies are available regarding different image processing techniques for automatic segmentation of the FAZ in various retinal imaging modalities [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], studies focusing on automatic FAZ segmentation in OCTA were usually conducted on healthy subjects. In addition, a few studies assessing the accuracy of FAZ delineation in OCTA images of diabetic eyes have obtained poor results due to the high incidence of signal noise and artefacts in OCTA imaging of diabetic patients [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. However, some authors such as Liu et al proposed the application of the watershed algorithm in FAZ segmentation for analysing and diagnosing eye diseases [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

A Reliable Criterion for the Correct Delimitation of the Foveal Avascular Zone in Diabetic Patients

Fernández-Espinosa,

Ruiz-Tabuenca,

Orduna-Hospital

et al. 2023

JPM

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Background: Manual segmentation of the Foveal Avascular Zone (FAZ) has a high level of variability. Research into retinas needs coherent segmentation sets with low variability. Methods: Retinal optical coherence tomography angiography (OCTA) images from type-1 diabetes mellitus (DM1), type-2 diabetes mellitus (DM2) and healthy patients were included. Superficial (SCP) and deep (DCP) capillary plexus FAZs were manually segmented by different observers. After comparing the results, a new criterion was established to reduce variability in the segmentations. The FAZ area and acircularity were also studied. Results: The new segmentation criterion produces smaller areas (closer to the real FAZ) with lower variability than the different criteria of the explorers in both plexuses for the three groups. This was particularly noticeable for the DM2 group with damaged retinas. The acircularity values were also slightly reduced with the final criterion in all groups. The FAZ areas with lower values showed slightly higher acircularity values. We also have a consistent and coherent set of segmentations with which to continue our research. Conclusions: Manual segmentations of FAZ are generally carried out with little attention to the consistency of the measurements. A novel criterion for segmenting the FAZ allows segmentations made by different observers to be more similar.

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“…However, challenges associated with the use of this method remain. First, many studies on the FAZ area segmentation in OCTA images were performed in healthy subjects [ 35 – 37 ], while the incidence of signal noise and artifacts in OCTA imaging of diabetic patients is higher than that in their counterparts; consequently, most of the FAZ measurements in OCTA images of diabetic eyes have low accuracy [ 37 , 38 ]. In medical image segmentation tasks, the most commonly used CNN model is based on U-Net [ 12 ], which consists of the contraction path of the capture context and symmetric expansion path to achieve precise positioning.…”

Section: Discussionmentioning

confidence: 99%

Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia

Meng

Lan

et al. 2022

Journal of Diabetes Research

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Objectives.The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose. Methods. The FAZ boundaries on the full-thickness retina of 6 × 6 mm en face OCTA images of DMI and normal eyes were manually marked. Seventy percent of OCTA images were used as the training set, and ten percent of these images were used as the validation set to train the improved U-Net CNN with two attention modules. Finally, twenty percent of the OCTA images were used as the test set to evaluate the accuracy of this model relative to that of the baseline U-Net model. This model was then applied to the public data set sFAZ to compare its effectiveness with existing models at identifying and quantifying the FAZ area. Results. This study included 110 OCTA images. The Dice score of the FAZ area predicted by the proposed method was 0.949, the Jaccard index was 0.912, and the area correlation coefficient was 0.996. The corresponding values for the baseline U-Net were 0.940, 0.898, and 0.995, respectively, and those based on the description data set sFAZ were 0.983, 0.968, and 0.950, respectively, which were better than those previously reported based on this data set. Conclusions. The improved U-Net CNN was more accurate at automatically measuring the FAZ area on the OCTA images than the traditional CNN. The present model may measure the DMI index more accurately, thereby assisting in the diagnosis and prognosis of retinal vascular diseases such as diabetic retinopathy.

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Modeling, Localization, and Segmentation of the Foveal Avascular Zone on Retinal OCT-Angiography Images

Cited by 5 publications

References 31 publications

Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

A Reliable Criterion for the Correct Delimitation of the Foveal Avascular Zone in Diabetic Patients

Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia

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