Depth-resolved visualization and automated quantification of hyperreflective foci on OCT scans using optical attenuation coefficients

The Total Macular Burden of Hyperreflective Foci and the Onset of Persistent Choroidal Hypertransmission Defects in Intermediate AMD

PURPOSE

The association between the total macular burden of hyperreflective foci (HRF) in eyes with intermediate AMD (iAMD) and the onset of persistent choroidal hypertransmission defects (hyperTDs) was studied using swept-source optical coherence tomography (SS-OCT).

DESIGN

Post hoc subgroup analysis of a prospective study.

METHODS

A retrospective review of iAMD eyes from subjects enrolled in a prospective SS-OCT study was performed. All eyes underwent 6×6 mm SS-OCT angiography (SS-OCTA) imaging at baseline and follow-up visits. En face sub-retinal pigment epithelium (subRPE) slabs with segmentation boundaries positioned 64-400 µm beneath Bruch's membrane (BM) were used to identify persistent choroidal hyperTDs. None of the eyes had persistent hyperTDs at baseline. The same subRPE slab was used to identify choroidal hypotransmission defects (hypoTDs) attributable to HRF located either intraretinally (iHRF) or along the RPE (rpeHRF) based on corresponding B-scans. A semiautomated algorithm was used by two independent graders to validate and refine the HRF outlines. The HRF area and the drusen volume within a 5mm fovea-centered circle were measured at each visit.

RESULTS

The median follow-up time for the 171 eyes from 121 patients included in this study was 59.1 months (95%CI: 52.0-67.8 months). Of these, 149 eyes (87%) had HRF, and 82 (48%) developed at least one persistent hyperTD during the follow-up. Although univariable Cox regression analyses showed that both drusen volume and total HRF area were associated with the onset of the first persistent hyperTD, multivariable analysis showed that the area of total HRF was the sole significant predictor for the onset of hyperTDs (P<0.001). ROC analysis identified an HRF area ≥ 0.07 mm² to predict the onset of persistent hyperTDs within one year with an area under the curve (AUC) of 0.661 (0.570-0.753), corresponding to a sensitivity of 55% and a specificity of 74% (P<0.001).

CONCLUSIONS

The total macular burden of HRF, which includes both the HRF along the RPE and within the retina, is an important predictor of disease progression from iAMD to the onset of persistent hyperTDs and should serve as a key OCT biomarker to select iAMD patients at high-risk for disease progression in future clinical trials.

Calcified Drusen Prevent the Detection of Underlying Choriocapillaris Using Swept-Source Optical Coherence Tomography Angiography

Purpose

In age-related macular degeneration (AMD), choriocapillaris flow deficits (CCFDs) under soft drusen can be measured using established compensation strategies. This study investigated whether CCFDs can be quantified under calcified drusen (CaD).

Methods

CCFDs were measured in normal eyes (n = 30) and AMD eyes with soft drusen (n = 30) or CaD (n = 30). CCFD density masks were generated to highlight regions with higher CCFDs. Masks were also generated for soft drusen and CaD based on both structural en face OCT images and corresponding B-scans. Dice similarity coefficients were calculated between the CCFD density masks and both the soft drusen and CaD masks. A phantom experiment was conducted to simulate the impact of light scattering that arises from CaD.

Results

Area measurements of CCFDs were highly correlated with those of CaD but not soft drusen, suggesting an association between CaD and underlying CCFDs. However, unlike soft drusen, the detected optical coherence tomography (OCT) signals underlying CaD did not arise from the defined CC layer but were artifacts caused by the multiple scattering property of CaD. Phantom experiments showed that the presence of highly scattering material similar to the contents of CaD caused an artifactual scattering tail that falsely generated a signal in the CC structural layer but the underlying flow could not be detected. Similarly, CaD also caused an artifactual scattering tail and prevented the penetration of light into the choroid, resulting in en face hypotransmission defects and an inability to detect blood flow within the choriocapillaris. Upon resolution of the CaD, the CC perfusion became detectable.

Conclusions

The high scattering property of CaD leads to a scattering tail under these drusen that gives the illusion of a quantifiable optical coherence tomography angiography signal, but this signal does not contain the angiographic information required to assess CCFDs. For this reason, CCFDs cannot be reliably measured under CaD, and CaD must be identified and excluded from macular CCFD measurements.

Spectral-Domain and Swept-Source OCT Angiographic Scans Yield Similar Drusen Measurements When Processed with the Same Algorithm

Purpose

An algorithm developed to obtain drusen area and volume measurements using swept-source OCT angiography (SS-OCTA) scans was tested on spectral-domain OCT angiography (SD-OCTA) scans.

Design

Retrospective study.

Participants

Forty pairs of scans from 27 eyes with intermediate age-related macular degeneration and drusen.

Methods

Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6 mm × 6 mm OCTA scan patterns. Using the same algorithm, we obtained drusen area and volume measurements within both 3 mm and 5 mm fovea-centered circles. Paired 2-sample t-tests were performed along with Pearson's correlation tests.

Main Outcome Measures

Mean square root (sqrt) drusen area and cube root (cbrt) drusen volume within the 3 mm and 5 mm fovea-centered circles.

Results

Mean sqrt drusen area values from SD-OCTA and SS-OCTA scans were 1.57 (standard deviation [SD] 0.57) mm and 1.49 (SD 0.58) mm in the 3 mm circle and 1.88 (SD 0.59) mm and 1.76 (SD 0.58) mm in the 5 mm circle, respectively. Mean cbrt drusen volume measurements were 0.54 (SD 0.19) mm and 0.51 (SD 0.20) mm in the 3 mm circle, and 0.60 (SD 0.17) mm and 0.57 (SD 0.17) mm in the 5 mm circle. Small differences in area and volume measurements were found (all P < 0.001); however, the correlations between the instruments were strong (all coefficients > 0.97; all P < 0.001).

Conclusions

An algorithm originally developed for SS-OCTA scans performs well when used to obtain drusen volume and area measurements from SD-OCTA scans; thus, a separate SD-OCT structural scan is unnecessary to obtain measurements of drusen.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Rediscovering Age-Related Macular Degeneration with Swept-Source OCT Imaging: The 2022 Charles L. Schepens, MD, Lecture

PURPOSE

Swept-source OCT angiography (SS-OCTA) scans of eyes with age-related macular degeneration (AMD) were used to replace color, autofluorescence, infrared reflectance, and dye-based fundus angiographic imaging for the diagnosis and staging of AMD. Through the use of different algorithms with the SS-OCTA scans, both structural and angiographic information can be viewed and assessed using both cross sectional and en face imaging strategies.

DESIGN

Presented at the 2022 Charles L. Schepens, MD, Lecture at the American Academy of Ophthalmology Retina Subspecialty Day, Chicago, Illinois, on September 30, 2022.

PARTICIPANTS

Patients with AMD.

METHODS

Review of published literature and ongoing clinical research using SS-OCTA imaging in AMD.

MAIN OUTCOME MEASURES

Swept-source OCT angiography imaging of AMD at different stages of disease progression.

RESULTS

Volumetric SS-OCTA dense raster scans were used to diagnose and stage both exudative and nonexudative AMD. In eyes with nonexudative AMD, a single SS-OCTA scan was used to detect and measure structural features in the macula such as the area and volume of both typical soft drusen and calcified drusen, the presence and location of hyperreflective foci, the presence of reticular pseudodrusen, also known as subretinal drusenoid deposits, the thickness of the outer retinal layer, the presence and thickness of basal laminar deposits, the presence and area of persistent choroidal hypertransmission defects, and the presence of treatment-naïve nonexudative macular neovascularization. In eyes with exudative AMD, the same SS-OCTA scan pattern was used to detect and measure the presence of macular fluid, the presence and type of macular neovascularization, and the response of exudation to treatment with vascular endothelial growth factor inhibitors. In addition, the same scan pattern was used to quantitate choriocapillaris (CC) perfusion, CC thickness, choroidal thickness, and the vascularity of the choroid.

CONCLUSIONS

Compared with using several different instruments to perform multimodal imaging, a single SS-OCTA scan provides a convenient, comfortable, and comprehensive approach for obtaining qualitative and quantitative anatomic and angiographic information to monitor the onset, progression, and response to therapies in both nonexudative and exudative AMD.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Effective biomarkers are required for assessing the progression of age-related macular degeneration (AMD), a prevalent and progressive eye disease. This paper presents a deep learning-based automated algorithm, applicable to both swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) scans, for measuring outer retinal layer (ORL) thickness as a surrogate biomarker for outer retinal degeneration, e.g., photoreceptor disruption, to assess AMD progression. The algorithm was developed based on a modified TransUNet model with clinically annotated retinal features manifested in the progression of AMD. The algorithm demonstrates a high accuracy with an intersection of union (IoU) of 0.9698 in the testing dataset for segmenting ORL using both SS-OCT and SD-OCT datasets. The robustness and applicability of the algorithm are indicated by strong correlation (r = 0.9551, P < 0.0001 in the central-fovea 3 mm-circle, and r = 0.9442, P < 0.0001 in the 5 mm-circle) and agreement (the mean bias = 0.5440 um in the 3-mm circle, and 1.392 um in the 5-mm circle) of the ORL thickness measurements between SS-OCT and SD-OCT scans. Comparative analysis reveals significant differences (P < 0.0001) in ORL thickness among 80 normal eyes, 30 intermediate AMD eyes with reticular pseudodrusen, 49 intermediate AMD eyes with drusen, and 40 late AMD eyes with geographic atrophy, highlighting its potential as an independent biomarker for predicting AMD progression. The findings provide valuable insights into the ORL alterations associated with different stages of AMD and emphasize the potential of ORL thickness as a sensitive indicator of AMD severity and progression.

Segmentation of paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images through weakly supervised deep convolutional networks

BACKGROUND AND OBJECTIVES

Spectral-domain optical coherence tomography (SD-OCT) is a valuable tool for non-invasive imaging of the retina, allowing the discovery and visualization of localized lesions, the presence of which is associated with eye diseases. The present study introduces X-Net, a weakly supervised deep-learning framework for automated segmentation of paracentral acute middle maculopathy (PAMM) lesions in retinal SD-OCT images. Despite recent advances in the development of automatic methods for clinical analysis of OCT scans, there remains a scarcity of studies focusing on the automated detection of small retinal focal lesions. Additionally, most existing solutions depend on supervised learning, which can be time-consuming and require extensive image labeling, whereas X-Net offers a solution to these challenges. As far as we can determine, no prior study has addressed the segmentation of PAMM lesions in SD-OCT images.

METHODS

This study leverages 133 SD-OCT retinal images, each containing instances of paracentral acute middle maculopathy lesions. A team of eye experts annotated the PAMM lesions in these images using bounding boxes. Then, labeled data were used to train a U-Net that performs pre-segmentation, producing region labels of pixel-level accuracy. To attain a highly-accurate final segmentation, we introduced X-Net, a novel neural network made up of a master and a slave U-Net. During training, it takes the expert annotated, and pixel-level pre-segment annotated images and employs sophisticated strategies to ensure the highest segmentation accuracy.

RESULTS

The proposed method was rigorously evaluated on clinical retinal images excluded from training and achieved an accuracy of 99% with a high level of similarity between the automatic segmentation and expert annotation, as demonstrated by a mean Intersection-over-Union of 0.8. Alternative methods were tested on the same data. Single-stage neural networks proved insufficient for achieving satisfactory results, confirming that more advanced solutions, such as the proposed method, are necessary. We also found that X-Net using Attention U-net for both the pre-segmentation and X-Net arms for the final segmentation shows comparable performance to the proposed method, suggesting that the proposed approach remains a viable solution even when implemented with variants of the classic U-Net.

CONCLUSIONS

The proposed method exhibits reasonably high performance, validated through quantitative and qualitative evaluations. Medical eye specialists have also verified its validity and accuracy. Thus, it could be a viable tool in the clinical assessment of the retina. Additionally, the demonstrated approach for annotating the training set has proven to be effective in reducing the expert workload.

Unleashing the power of optical attenuation coefficients to facilitate segmentation strategies in OCT imaging of age-related macular degeneration: perspective

The use of optical attenuation coefficients (OAC) in optical coherence tomography (OCT) imaging of the retina has improved the segmentation of anatomic layers compared with traditional intensity-based algorithms. Optical attenuation correction has improved our ability to measure the choroidal thickness and choroidal vascularity index using dense volume scans. Algorithms that combine conventional intensity-based segmentation with depth-resolved OAC OCT imaging have been used to detect elevations of the retinal pigment epithelium (RPE) due to drusen and basal laminar deposits, the location of hyperpigmentation within the retina and along the RPE, the identification of macular atrophy, the thickness of the outer retinal (photoreceptor) layer, and the presence of calcified drusen. OAC OCT algorithms can identify the risk-factors that predict disease progression in age-related macular degeneration.

Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images

The tear fluid reservoir (TFR) under the sclera lens is a unique characteristic providing optical neutralization of any aberrations from corneal irregularities. Anterior segment optical coherence tomography (AS-OCT) has become an important imaging modality for sclera lens fitting and visual rehabilitation therapy in both optometry and ophthalmology. Herein, we aimed to investigate whether deep learning can be used to segment the TFR from healthy and keratoconus eyes, with irregular corneal surfaces, in OCT images. Using AS-OCT, a dataset of 31850 images from 52 healthy and 46 keratoconus eyes, during sclera lens wear, was obtained and labeled with our previously developed algorithm of semi-automatic segmentation. A custom-improved U-shape network architecture with a full-range multi-scale feature-enhanced module (FMFE-Unet) was designed and trained. A hybrid loss function was designed to focus training on the TFR, to tackle the class imbalance problem. The experiments on our database showed an IoU, precision, specificity, and recall of 0.9426, 0.9678, 0.9965, and 0.9731, respectively. Furthermore, FMFE-Unet was found to outperform the other two state-of-the-art methods and ablation models, suggesting its strength in segmenting the TFR under the sclera lens depicted on OCT images. The application of deep learning for TFR segmentation in OCT images provides a powerful tool to assess changes in the dynamic tear film under the sclera lens, improving the efficiency and accuracy of lens fitting, and thus supporting the promotion of sclera lenses in clinical practice.

Automated segmentation and quantification of calcified drusen in 3D swept source OCT imaging

Qualitative and quantitative assessments of calcified drusen are clinically important for determining the risk of disease progression in age-related macular degeneration (AMD). This paper reports the development of an automated algorithm to segment and quantify calcified drusen on swept-source optical coherence tomography (SS-OCT) images. The algorithm leverages the higher scattering property of calcified drusen compared with soft drusen. Calcified drusen have a higher optical attenuation coefficient (OAC), which results in a choroidal hypotransmission defect (hypoTD) below the calcified drusen. We show that it is possible to automatically segment calcified drusen from 3D SS-OCT scans by combining the OAC within drusen and the hypoTDs under drusen. We also propose a correction method for the segmentation of the retina pigment epithelium (RPE) overlying calcified drusen by automatically correcting the RPE by an amount of the OAC peak width along each A-line, leading to more accurate segmentation and quantification of drusen in general, and the calcified drusen in particular. A total of 29 eyes with nonexudative AMD and calcified drusen imaged with SS-OCT using the 6 × 6 mm² scanning pattern were used in this study to test the performance of the proposed automated method. We demonstrated that the method achieved good agreement with the human expert graders in identifying the area of calcified drusen (Dice similarity coefficient: 68.27 ± 11.09%, correlation coefficient of the area measurements: r = 0.9422, the mean bias of the area measurements = 0.04781 mm²).

Does the Outer Retinal Thickness Around Geographic Atrophy Represent Another Clinical Biomarker for Predicting Growth?

PURPOSE

To determine whether the outer retinal layer (ORL) thickness around geographic atrophy (GA) could serve as a clinical biomarker to predict the annual enlargement rate (ER) of GA.

DESIGN

Retrospective analysis of a prospective, observational case series.

METHODS

Eyes with GA were imaged with a swept-source OCT 6 × 6 mm scan pattern. GA lesions were measured from customized en face OCT images and the annual ERs were calculated. The ORL was defined and segmented from the inner boundary of outer plexiform layer (OPL) to the inner boundary of retinal pigment epithelium (RPE) layer. The ORL thickness was measured at different subregions around GA.

RESULTS

A total of 38 eyes from 27 participants were included. The same eyes were used for the choriocapillaris (CC) flow deficit (FD) analysis and the RPE to the Bruch membrane (RPE-BM) distance measurements. A negative correlation was observed between the ORL thickness and the GA growth. The ORL thickness in a 300-μm rim around GA showed the strongest correlation with the GA growth (r = -0.457, P = .004). No correlations were found between the ORL thickness and the CC FDs; however, a significant correlation was found between the ORL thickness and the RPE-BM distances around GA (r = -0.398, P = .013).

CONCLUSIONS

ORL thickness showed a significant negative correlation with annual GA growth, but also showed a significant correlation with the RPE-BM distances, suggesting that they were dependently correlated with GA growth. This finding suggests that the loss of photoreceptors was associated with the formation of basal laminar deposits around GA.

Integrating a pressure sensor with an OCT handheld probe to facilitate imaging of microvascular information in skin tissue beds

Optical coherence tomography (OCT) and OCT angiography (OCTA) have been increasingly applied in skin imaging applications in dermatology, where the imaging is often performed with the OCT probe in contact with the skin surface. However, this contact mode imaging can introduce uncontrollable mechanical stress applied to the skin, inevitably complicating the interpretation of OCT/OCTA imaging results. There remains a need for a strategy for assessing local pressure applied on the skin during imaging acquisition. This study reports a handheld scanning probe integrated with built-in pressure sensors, allowing the operator to control the mechanical stress applied to the skin in real-time. With real time feedback information, the operator can easily determine whether the pressure applied to the skin would affect the imaging quality so as to obtain repeatable and reliable OCTA images for a more accurate investigation of skin conditions. Using this probe, imaging of palm skin was used in this study to demonstrate how the OCTA imaging would have been affected by different mechanical pressures ranging from 0 to 69 kPa. The results showed that OCTA imaging is relatively stable when the pressure is less than 11 kPa, and within this range, the change of vascular area density calculated from the OCTA imaging is below 0.13%. In addition, the probe was used to augment the OCT monitoring of blood flow changes during a reactive hyperemia experiment, in which the operator could properly control the amount of pressure applied to the skin surface and achieve full release after compression stimulation.