Dataset Information

Automatic detection of the foveal center in optical coherence tomography.

ABSTRACT: We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels. For 391 scans (97.75%) the method identified the foveal center with a distance to a human reference less than 750 μm, with a mean (± SD) distance of 71 μm ± 107 μm. Two independent observers also annotated the foveal center, with a mean distance to the reference of 57 μm ± 84 μm and 56 μm ± 80 μm, respectively. Furthermore, we evaluate variations to the proposed network architecture and training procedure, providing insight in the characteristics that led to the demonstrated performance of the proposed method.

SUBMITTER: Liefers B

PROVIDER: S-EPMC5695961 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Automatic detection of the foveal center in optical coherence tomography.

Liefers Bart B Venhuizen Freerk G FG Schreur Vivian V van Ginneken Bram B Hoyng Carel C Fauser Sascha S Theelen Thomas T Sánchez Clara I CI

Biomedical optics express 20171023 11

We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the prop ...[more]

PMID: 29188111

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Project description:BackgroundTo describe the features of foveal microvasculature using optical coherence tomography angiography (OCTA) and to determine the related clinical factors in eyes with surgically closed macular hole (MH).MethodsA retrospective case series of 18 patients with unilateral MH was reviewed. The patients maintained complete hole closure after vitrectomy with inner limiting membrane (ILM) peeling for at least 12 months. The healthy fellow eyes were studied as controls. The foveal microvasculature of both eyes was examined by OCTA. The area of the foveal avascular zone (FAZ) and the vascular density (VD) ratio in the superficial and deep capillary plexuses (SCP and DCP) were determined after surgery. Several clinical factors including age, stage and dimensions of MH, papillofoveal distance, the extent of nasal displacement of the fovea after surgery, postoperative central foveal thickness, and outer-retina integrity were evaluated to determine any relationships with the OCTA parameters.ResultsThe mean FAZ area in both the SCP and DCP (0.29 ± 0.11 mm2 and 0.39 ± 0.14 mm2) was significantly smaller than those of the controls (0.45 ± 0.14 mm2 and 0.62 ± 0.22 mm2) (p = 0.001 and <0.001, respectively). The mean VD ratio in the SCP (0.270 ± 0.349) was similar to that of the controls (0.321 ± 0.189) (p = 0.231); however, that in the DCP (0.321 ± 0.189) was significantly lower than that of the controls (0.331 ± 0.119) (p = 0.025). Only the extent of nasal displacement of the fovea was correlated with the DCP FAZ-area difference values between the study group and the controls (correlation coefficient = 0.577; p = 0.012).ConclusionsAfter successful MH surgery, the FAZ area in both the SCP and DCP was smaller and the VD ratio of the DCP was lower, suggesting a possible DCP vulnerability to tractional stress. As the FAZ area of the DCP in closed-MH eyes became smaller than that in the controls, the fovea was less displaced toward the optic disc, possibly reflecting a lack of retinal redundancy caused by horizontal stretching accompanied by foveal displacement.

Project description:Importance:Whether optical coherence tomography angiography (OCT-A) outperforms OCT to detect glaucoma remains inconclusive. Objective:To compare (1) the diagnostic performance for detection of glaucoma and (2) the structure-function association between inner macular vessel density and inner macular thickness. Design, Setting, and Participants:This cross-sectional study included 115 patients with glaucoma and 35 healthy individuals for measurements of retinal thickness and retinal vessel density, segmented between the anterior boundary of internal limiting membrane and the posterior boundary of the inner plexiform layer, over the 3 × 3-mm2 macula using swept-source OCT. All participants were Chinese. Visual sensitivity corresponding to the 3 × 3-mm2 macular region was expressed in unlogged 1/lambert for investigation of the structure-function associations. Diagnostic performance was evaluated with area under the receiver operating characteristic curves (AUCs). The study was conducted between January 12, 2016, and December 12, 2016. Main Outcomes and Measures:Area under the receiver operating characteristic curve and R2 analysis. Results:Of the 115 patients with glaucoma, 42 (36.5%) were women (mean [SD] age, 53.5 [13.4] years); of the 35 individuals with healthy eyes, 25 (71.4%) were women (age, 60.6 [5.9] years). Inner macular vessel density and thickness were 4.3% (95% CI, 2.4%-6.1%) and 21.1 μm (95% CI, 17.4-24.9 μm) smaller, respectively, in eyes with glaucoma compared with healthy eyes. The AUC of mean inner macular thickness for glaucoma detection was greater than that of mean inner macular vessel density (difference, 0.17; 95% CI, 0.01-0.31; P = .03). At 90% specificity, the sensitivity of mean inner macular thicknesses for detection of glaucoma was greater than that of mean inner macular vessel densities (difference, 29.2%; 95% CI, 11.5%-64.6%; P = .02). The strength of the structure-function association was stronger for mean inner macular thickness than mean inner macular vessel density in the linear (difference in R2 = 0.38; 95% CI, 0.22-0.54; P < .001) and nonlinear (difference in R2 = 0.36; 95% CI, 0.21-0.51; P < .001) regression models. Conclusions and Relevance:In this study, OCT measurement of inner macular thickness shows a higher diagnostic performance to detect glaucoma and a stronger structure-function association than the currently used OCT-A measurement of inner macular vessel density. These findings may suggest that OCT-A of the macula has a limited role in the diagnostic evaluation of glaucoma.

Project description: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.

Dataset Information

Automatic detection of the foveal center in optical coherence tomography.

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Automatic detection of the foveal center in optical coherence tomography.

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