Optical Coherence Tomography Angiography (OCTA), a functional extension of OCT, has the potential to replace most invasive fluorescein angiography (FA) exams in ophthalmology. So far, OCTA's field of view is however still lacking behind fluorescence fundus photography techniques. This is problematic, because many retinal diseases manifest at an early stage by changes of the peripheral retinal capillary network. It is therefore desirable to expand OCTA's field of view to match that of ultrawidefield fundus cameras. We present a custom developed clinical high-speed swept-source OCT (SS-OCT) system operating at an acquisition rate 8-16 times faster than today's state-of-the-art commercially available OCTA devices. Its speed allows us to capture ultra-wide fields of view of up to 90 degrees with an unprecedented sampling density and hence extraordinary resolution by merging two single shot scans with 60 degrees in diameter. To further enhance the visual appearance of the angiograms, we developed for the first time a three-dimensional deep learning based algorithm for denoising volumetric OCTA data sets. We showcase its imaging performance and clinical usability by presenting images of patients suffering from diabetic retinopathy.
AimTo assess the detection rate of retinal neovascularisation (NV) in eyes with proliferative diabetic retinopathy (PDR) using widefield optical coherence tomography angiography (WF-OCTA) in comparison to ultrawidefield fluorescein angiography (UWF-FA).MethodsSingle-capture 65°-WF-OCTA-imaging was performed in patients with NV at the disc or elsewhere (NVE) detected on UWF-FA using a modified PlexElite system and B-scans were examined for blood flow signals breaching the internal limiting membrane. Sensitivity of WF-OCTA and UWF colour fundus (UWF-CF) photography for correct diagnosis of PDR was determined and interdevice agreement (Fleiss’ κ) between WF-OCTA and UWF-FA for detection of NV in the total gradable area and each retinal quadrant was evaluated.ResultsFifty-nine eyes of 41 patients with PDR detected on UWF-FA were included. Sensitivity of detecting PDR on WF-OCTA scans was 0.95 in contrast to 0.78 on UWF-CF images. Agreement in detecting NVE between WF-OCTA and UWF-FA was high in the superotemporal (κ=0.98) and inferotemporal (κ=0.94) and weak in the superonasal (κ=0.24) and inferonasal quadrants (κ=0.42). On UWF-FA, 63% of NVEs (n=153) were located in the temporal quadrants with 93% (n=142) of them being detected on WF-OCTA scans.ConclusionThe high reliability of non-invasive WF-OCTA imaging in detecting PDR can improve clinical examination with the potential to replace FA as a single diagnostic tool.
Purpose: To evaluate neuroretinal integrity in different subtypes of optical coherence tomography (OCT)-graded partial-thickness macular holes. Methods: Fovea-centred SD-OCT images (Cirrus, Carl Zeiss Meditec AG; Spectralis, Heidelberg Engineering GmbH) and visual acuity (VA) acquired at every visit were analysed by two retina specialists retrospectively in 71 eyes of 65 patients. Partial-thickness macular holes were classified as lamellar macular hole (LMH), epiretinal membrane foveoschisis (ERMF) or macular pseudohole (MPH). Results: Lamellar macular hole, ERMF and MPH were diagnosed in 33 (47%), 31 (43%) and 7 (10%) eyes with a VA of 0.18 AE 0.25, 0.15 AE 0.2, and 0.06 AE 0.08 (p = 0.323), respectively. Median follow-up time was 11 (interquartile range 4-32.5), 10 (interquartile range 5-18) and 19 (interquartile range 8-24) months in LMH, ERMF and MPH. In all subgroups, VA remained stable during the follow-up (p = 0.652, p = 0.915 and p = 1.000). Epiretinal proliferations (EP) were present in 12 LMH and 3 ERMF. At baseline, eyes with EP had significantly worse VA (p < 0.001), wider foveal cavities (p = 0.007) and thinner foveal floors (p < 0.001) compared with eyes without EP. Twelve out of 15 eyes with EP showed exudative cystoid spaces. Among all 71 eyes, 51 remained morphologically and functionally stable during follow-up. Conclusion: In our study cohort, EP are associated with worse VA and advanced neuroretinal tissue loss presenting with wider foveal cavities and thinner foveal floors. During the follow-up period, VA remained stable in all entities of partialthickness macular holes.
Comparison of two ultra-widefield (UWF) color-fundus (CF) imaging devices in diabetic patients for visualization of retinal periphery and detection of early microvascular lesions. The total gradable areas (TGA) seen on non-mydriatic CF-images of two UWF-imaging devices (Optos Daytona P200T; Zeiss Clarus 700) were compared and differences in projected area measured. Retinal periphery outside the 7 standard fields (7SF) was divided into: F3 temporal, F4 superotemporal, F5 inferotemporal, F6 superonasal, F7 inferonasal. DR stage was evaluated in the 7SF and the TGA on images of both devices and compared using Cohens κ. 67 eyes of 67 patients (52.5 ± 15.3 years) were analysed. DR stages in the 7SF were no (n = 36 Optos, n = 35 Clarus), mild (n = 16 Optos, n = 17 Clarus), and moderate DR (n = 15). Optos depicted significantly more area in F3 (median [interquartile range]; 2.41% [1.06–4.11] vs 0% [0–0], P < 0.001) and Clarus in F7 (3.29% [0–7.69] vs 0% [0–3.27], P = 0.002). In 4 eyes DR-stage was higher using Optos due to peripheral lesions not seen on the Clarus. Interrater reliability of DR-stage on both devices was almost perfect in the 7SF (κ = 0.975) and the TGA (κ = 0.855). Reliability in detecting signs of early DR is high on both devices. Clarus allowed for better visualization of the inferonasal field, Optos of the temporal field.
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical coherence tomography scans that enables the automated assessment of large amounts of data. Evaluation on manually annotated 2D en face images of the retina demonstrates that training of a standard U-Net can yield improved optic nerve head segmentation and localization performance when the underlying pixel-level binary classification task is spatially relaxed through domain knowledge.
We present a multiple instance learning-based network, MIL-ResNet14, detecting biomarkers for diabetic retinopathy in a widefield optical coherence tomography angiography dataset with high accuracy, without the necessity of annotations other than the information of whether a scan stems from a diabetic patient or not. Previously introduced deep learning-based classifiers were able to support the detection of diabetic biomarkers in OCTA images, however, require expert labeling on a pixel-level, a labor-intensive and expensive process. We evaluated our proposed architecture against two proven-capable classifiers, ResNet14 and VGG16. The dataset we applied for this study was acquired with a MHz A-Scan rate widefield Swept Source-OCT device. We utilized a total of 352 en face images, displaying retinal vasculature over a field of view of 18 mm x 18 mm. MIL-ResNet14 outperformed both other networks with an F-score of 0.95, a precision of 0.909 and an Area Under the Curve of 0.973. In addition, we could show via saliency overlays of gradient-weighted class activation mappings onto the en face images, that MIL-ResNet14 pays special attention to clinically relevant biomarkers like ischemic areas and retinal vessel anomalies. This could therefore function as a vigorous diagnostic decision support tool for clinical ophthalmologic screenings.
Previously introduced deep learning classifiers were able to support diabetic biomarker detection in OCTA en face images, but require pixel-by-pixel expert labeling, which is a labor-intensive and expensive process. We present a multiple-instance learning-based network, MIL-ResNet,14 that detects clinically relevant diabetic retinopathy biomarkers in a wide-angle (65°) OCTA dataset with high accuracy without annotation. We evaluated our proposed architecture against two well-established machine learning classifiers, ResNet14 and VGG16. The dataset we used for this study was acquired with a MHz A-scan rate swept source OCT device. We used a total of 352 en face images representing the retinal vasculature over an 18 mm x 18 mm field of view. MIL-ResNet14 outperformed the other two networks with an F-score of 0.95, a precision of 0.909 and an area under the curve of 0.973. In addition, we were able to demonstrate that MIL-ResNet14 paid special attention to relevant biomarkers such as ischemic areas and retinal vascular abnormalities by saliency overlay of gradient-weighted class activation maps on top of the en face images. Thus, OCTA could be used as a powerful diagnostic decision support tool for clinical ophthalmic screening in combination with our MIL approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.