101 research outputs found
Active and inactive microaneurysms identified and characterized by structural and angiographic optical coherence tomography
Purpose: To characterize flow status within microaneurysms (MAs) and
quantitatively investigate their relations with regional macular edema in
diabetic retinopathy (DR). Design: Retrospective, cross-sectional study.
Participants: A total of 99 participants, including 23 with mild
nonproliferative DR (NPDR), 25 with moderate NPDR, 34 with severe NPDR, 17 with
proliferative DR. Methods: In this study, 3x3-mm optical coherence tomography
(OCT) and OCT angiography (OCTA) scans with a 400x400 sampling density from one
eye of each participant were obtained using a commercial OCT system. Trained
graders manually identified MAs and their location relative to the anatomic
layers from cross-sectional OCT. Microaneurysms were first classified as active
if the flow signal was present in the OCTA channel. Then active MAs were
further classified into fully active and partially active MAs based on the flow
perfusion status of MA on en face OCTA. The presence of retinal fluid near MAs
was compared between active and inactive types. We also compared OCT-based MA
detection to fundus photography (FP) and fluorescein angiography (FA)-based
detection. Results: We identified 308 MAs (166 fully active, 88 partially
active, 54 inactive) in 42 eyes using OCT and OCTA. Nearly half of the MAs
identified straddle the inner nuclear layer and outer plexiform layer. Compared
to partially active and inactive MAs, fully active MAs were more likely to be
associated with local retinal fluid. The associated fluid volumes were larger
with fully active MAs than with partially active and inactive MAs. OCT/OCTA
detected all MAs found on FP. While not all MAs seen with FA were identified
with OCT, some MAs seen with OCT were not visible with FA or FP. Conclusions:
Co-registered OCT and OCTA can characterize MA activities, which could be a new
means to study diabetic macular edema pathophysiology
Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.
PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. METHODS: In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. RESULTS: Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). CONCLUSIONS: In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52
Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map
Deep learning classifiers provide the most accurate means of automatically
diagnosing diabetic retinopathy (DR) based on optical coherence tomography
(OCT) and its angiography (OCTA). The power of these models is attributable in
part to the inclusion of hidden layers that provide the complexity required to
achieve a desired task. However, hidden layers also render algorithm outputs
difficult to interpret. Here we introduce a novel biomarker activation map
(BAM) framework based on generative adversarial learning that allows clinicians
to verify and understand classifiers decision-making. A data set including 456
macular scans were graded as non-referable or referable DR based on current
clinical standards. A DR classifier that was used to evaluate our BAM was first
trained based on this data set. The BAM generation framework was designed by
combing two U-shaped generators to provide meaningful interpretability to this
classifier. The main generator was trained to take referable scans as input and
produce an output that would be classified by the classifier as non-referable.
The BAM is then constructed as the difference image between the output and
input of the main generator. To ensure that the BAM only highlights
classifier-utilized biomarkers an assistant generator was trained to do the
opposite, producing scans that would be classified as referable by the
classifier from non-referable scans. The generated BAMs highlighted known
pathologic features including nonperfusion area and retinal fluid. A fully
interpretable classifier based on these highlights could help clinicians better
utilize and verify automated DR diagnosis.Comment: 12 pages, 8 figure
Deep learning for optical coherence tomography angiography: Quantifying microvascular changes in diabetic retinopathy
Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. Machine learning applications have directly benefited ophthalmology, leveraging large amounts of data to create frameworks to aid clinical decision-making. In this thesis, several techniques to quantify the retinal microvasculature are explored. First, high-quality, averaged, 6x6mm OCT-A enface images are used to produce manual segmentations for the corresponding lower-quality, single-frame images to produce more training data. Using transfer learning, the resulting convolutional neural network (CNN) segmented the superficial capillary plexus and deep vascular complex with performance exceeding inter-rater comparisons. Next, a federated learning framework was designed to allow for collaborative training by multiple participants on a de-centralized data corpus. When trained for microvasculature segmentation, the framework achieved comparable performance to a CNN trained on a fully-centralized dataset
Age-Related Macular Degeneration and Diabetic Retinopathy
This reprint includes contributions from leaders in the field of personalized medicine in ophthalmology. The contributions are diverse and cover pre-clinical and clinical topics. We hope you enjoy reading the articles
Automatic macular edema identification and characterization using OCT images
© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Samagaio, G., Estévez, A., Moura, J. de, Novo, J., Fernández, M. I., & Ortega, M. (2018). “Automatic macular edema identification and characterization using OCT images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 163, 47–63. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2018.05.033.[Abstract]: Background and objective: The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments. Methods: This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence. Results: The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset. Conclusions: The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04
Biomarkers in Early Response to Brolucizumab on Pigment Epithelium Detachment Associated with Exudative Age-Related Macular Degeneration.
The purpose of this study was to describe early changes in the morphology of pigment epithelium detachments (PED) after an intravitreal injection of Brolucizumab into eyes with macular neovascularization secondary to exudative age-related macular degeneration (e-AMD).
We included twelve eyes of 12 patients with PED secondary to e-AMD which were not responding to prior anti-VEGF treatments. An ophthalmic examination and an assessment of PED-horizontal maximal diameter (PED-HMD), PED-maximum high (PED-MH) and macular neovascularization (MNV) flow area (MNV-FA) by the means of structural optical coherence tomography (OCT) and OCT Angiography (OCT-A) were performed at baseline, as well as 1, 7, 14 and 30 days after the injection.
The mean age of the population of study was 78.4 (SD ± 4.8). The mean number of previous Ranibizumab or Aflibercept injections was 13 (SD ± 8). At the last follow-up visit, the PED-HMD did not significantly change (p = 0.16; F(DF:1.94, 20,85) = 1.9), the PED-MH showed a significant reduction [p = 0.01; F(DF:1.31, 14.13) = 6.84.] and the MNV-FA did not significantly differ (p = 0.1; F(1.97, 21.67) = 2.54) from baseline. No signs of ocular inflammation were observed during follow-up.
A single Brolucizumab injection was able to determine the short-term effects on PEDs' anatomical features of eyes with an unresponsive e-AMD
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