Optical Coherence Tomography Segmentation Errors of the Retinal Nerve Fiber Layer Persist Over Time

Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography

Purpose

To assess the diagnostic performance and structure-function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma.

Methods

This cross-sectional pilot study analyzed 170 eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity.

Results

The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure-function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure-function association was similar when using RNFLT and RR.

Conclusions

RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice.

Translational Relevance

We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.

OCT Segmentation Errors with Bruch's Membrane Opening-Minimum Rim Width as Compared with Retinal Nerve Fiber Layer Thickness

OBJECTIVE

To compare the magnitude and location of automated segmentation errors of the Bruch's membrane opening-minimum rim width (BMO-MRW) and retinal nerve fiber layer thickness (RNFLT).

DESIGN

Cross-sectional study.

PARTICIPANTS

We included 162 glaucoma suspect or open-angle glaucoma eyes from 162 participants.

METHODS

We used spectral-domain optic coherence tomography (Spectralis 870 nm, Heidelberg Engineering) to image the optic nerve with 24 radial optic nerve head B-scans and a 12-degree peripapillary circle scan, and exported the native "automated segmentation only" results for BMO-MRW and RNFLT. We also exported the results after "manual refinement" of the measurements.

MAIN OUTCOME MEASURES

We calculated the absolute and proportional error globally and within the 12 30-degree sectors of the optic disc. We determined whether the glaucoma classifications were different between BMO-MRW and RNFLT as a result of manual and automatic segmentation.

RESULTS

The absolute error mean was larger for BMO-MRW than for RNFLT (10.8 μm vs. 3.58 μm, P < 0.001). However, the proportional errors were similar (4.3% vs. 4.4%, P = 0.47). In a multivariable regression model, errors in BMO-MRW were not significantly associated with age, location, magnitude, or severity of glaucoma loss (all P ≥ 0.05). However, larger RNFLT errors were associated with the superior and inferior sector location, thicker nerve fiber layer, and worse visual field (all P < 0.05). Errors in BMO-MRW and RNFLT were not likely to occur in the same sector location (R2 = 0.001; P = 0.15). With manual refinement, the glaucoma classification changed in 7.8% and 6.2% of eyes with BMO-MRW and RNFLT, respectively.

CONCLUSIONS

Both BMO-MRW and RNFLT measurements included segmentation errors, which did not seem to have a common location, and may result in differences in glaucoma classification.

FINANCIAL DISCLOSURE(S)

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

Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis

This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neuritis (ON). The training/validation dataset included 750 RNFL OCT B-scans. Performance of our algorithm was evaluated on 194 OCT B-scans from 70 healthy eyes, 82 scans from 28 NAION eyes, and 84 scans of 29 ON eyes. Results were compared to manual segmentation as a ground-truth and to RNFL calculations from the built-in instrument software. The Dice coefficient for the test images was 0.87. The mean average RNFL thickness using our U-Net was not different from the manually segmented best estimate and OCT machine data in control and ON eyes. In NAION eyes, while the mean average RNFL thickness using our U-Net algorithm was not different from the manual segmented value, the OCT machine data were different from the manual segmented values. In NAION eyes, the MAE of the average RNFL thickness was 1.18 ± 0.69 μm and 6.65 ± 5.37 μm in the U-Net algorithm segmentation and the conventional OCT machine data, respectively (P = 0.0001).

Clinicians' Use of Quantitative Information When Assessing the Rate of Structural Progression in Glaucoma

PURPOSE

OCT scans contain large amounts of information, but clinicians often rely on reported layer thicknesses when assessing the rate of glaucomatous progression. We sought to determine which of these quantifications most closely relate to the subjective assessment of glaucoma experts who had all the diagnostic information available.

DESIGN

Prospective cohort study.

PARTICIPANTS

Eleven glaucoma specialists independently scored the rate of structural progression from a series of 5 biannual clinical OCT printouts.

METHODS

A total of 100 glaucoma or glaucoma suspect eyes of 51 participants were included; 20 were scored twice to assess repeatability. Scores ranged from 1 (improvement) to 7 (very rapid progression). Generalized estimating equation linear models were used to predict the mean clinician score from the rates of change of retinal nerve fiber layer thickness (RNFLT) or minimum rim width (MRW) globally or in the most rapidly thinning of the 6 sectors.

MAIN OUTCOME MEASURES

The correlation between the objective rates of change and the average of the 11 clinicians' scores.

RESULTS

Average RNFLT within the series of study eyes was 79.3 μm (range, 41.4-126.6). Some 95% of individual clinician scores varied by ≤ 1 point when repeated. The mean clinician score was more strongly correlated with the rate of change of RNFLT in the most rapidly changing sector in %/year (pseudo-R2 = 0.657) than the rate of global RNFLT (0.372). The rate of MRW in the most rapidly changing sector had pseudo-R2 = 0.149.

CONCLUSIONS

The rate of change of RNFLT in the most rapidly changing sector predicted experts' assessment of the rate of structural progression better than global rates or MRW. Sectoral rates may be a useful addition to current clinical printouts.

Contrast-to-Noise Ratios to Evaluate the Detection of Glaucomatous Progression in the Superior and Inferior Hemiretina

Purpose

To determine the sensitivity of optical coherence tomography (OCT) and standard automated perimetry (SAP) for detecting glaucomatous progression in the superior and inferior hemiretina.

Methods

We calculated contrast-to-noise ratios (CNRs) for OCT retinal nerve fiber layer (RNFL) thickness of hemiretinas and for SAP mean total deviation (MTD) of the corresponding hemifields from longitudinal data (205 eyes, 125 participants). The glaucoma stage for each hemiretina was based on the corresponding hemifield's MTD. Contrast was defined as the difference of the parameter between two consecutive glaucoma stages, whereas noise was the measurement variability of the parameter in those stages. The higher the CNR of a parameter, the more sensitive it is to detecting progression in the transition between successive stages.

Results

There were no statistically significant differences for the RNFL CNR and MTD CNR between superior and inferior hemiretinas. As the glaucoma stage of the opposite hemiretina worsened, the MTD CNR in the transition from moderate to advanced glaucoma significantly increased. The RNFL CNR in the transition from mild to moderate glaucoma significantly decreased in case of advanced glaucoma in the opposite hemiretina.

Conclusions

Similar to full retinas, detecting conversion to glaucoma in hemiretinas is more sensitive with OCT than SAP, whereas with more advanced disease, SAP is more sensitive for detecting progression. More importantly, the sensitivity for detecting progression in one hemiretina with either technique depends on the glaucoma severity in the opposite hemiretina.

Translational Relevance

Monitoring glaucomatous progression with either OCT or SAP partly depends on the glaucoma severity in the opposite hemiretina.

The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage

Purpose

The purpose of this study was to improve the diagnostic ability of the optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability (p-) map by understanding the frequency and pattern of artifacts seen on the p-maps of healthy control (HC) eyes resembling glaucomatous damage.

Methods

RNFL p-maps were generated from wide-field OCT cube scans of 2 groups of HC eyes, 200 from a commercial normative group (HC-norm) and 54 from a prospective study group, as well as from 62 patient eyes, which included 32 with early glaucoma (EG). These 32 EG eyes had 24-2 mean deviation (MD) better than -6 dB and perimetric glaucoma as defined by 24-2 and 10-2 criteria. For the HC groups, "glaucoma-like" arcuates were defined as any red region near the temporal half of the disc.

Results

Seven percent of the 200 HC-norm and 11% of the 54 HC RNFL p-maps satisfied the definition of "glaucoma-like," as did all the patients' p-maps. The HC p-maps showed two general patterns of abnormal regions, "arcuate" and "temporal quadrant," and these patterns resembled those seen on some of the RNFL p-maps of the EG eyes. A "vertical midline" rule, which required the abnormal region to cross the vertical midline through the fovea, had a specificity of >99%, and a sensitivity of 75% for EG and 93% for moderate to advanced eyes.

Conclusions

Glaucoma-like artifacts on RNFL p-maps are relatively common and can masquerade as arcuate and/or widespread/temporal damage.

Translational Relevance

A vertical midline rule had excellent specificity. However, other OCT information is necessary to obtain high sensitivity, especially in eyes with early glaucoma.

Detection of Early Glaucomatous Damage: Performance of Summary Statistics From Optical Coherence Tomography and Perimetry

Purpose

To evaluate the diagnostic performance of optical coherence tomography (OCT) and visual field (VF) summary statistics (metrics) that are available in OCT and VF reports.

Methods

OCT disc and macular scans and 24-2 and 10-2 VFs were obtained from 56 healthy control (HC) eyes/participants and 61 eyes/patients with 24-2 mean deviation of better than –6 dB. All metrics were obtained from OCT radial, circle, and posterior pole cube scans and 24-2 and 10-2 VFs. Their diagnostic performances were evaluated, in isolation and in combinations. For specificity, the 56 HC eyes were used. For sensitivity, 40 of the 61 patient eyes were deemed likely glaucomatous based on an automated topographic method that evaluates structure–function (S–F) agreement. Any 1 of these 40 eyes not judged as abnormal by any given metric was considered a false negative.

Results

All single OCT and VF metrics misclassified HCs as glaucomatous and missed likely glaucomatous eyes. The best performing single metric was the temporal inferior thickness of the 3.5-mm circle scan, with 96% specificity and 83% sensitivity. Combinations of OCT–OCT and OCT–VF metrics markedly improved specificity. A newly proposed metric that evaluates structure–structure (S–S) agreement at a hemifield level had the highest accuracy. This S–S metric had 98% specificity and 80% sensitivity.

Conclusions

OCT and VF metrics, single or in combinations, have only moderate sensitivity for eyes with early glaucoma.

Translational Relevance

OCT and VF metrics combinations evaluating S–S or S–F agreement can be highly specific, which is an important implication for clinical and research purposes.

Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development

A method for detecting glaucoma based only on optical coherence tomography (OCT) is of potential value for routine clinical decisions, for inclusion criteria for research studies and trials, for large-scale clinical screening, as well as for the development of artificial intelligence (AI) decision models. Recent work suggests that the OCT probability (p-) maps, also known as deviation maps, can play a key role in an OCT-based method. However, artifacts seen on the p-maps of healthy control eyes can resemble patterns of damage due to glaucoma. We document in section 2 that these glaucoma-like artifacts are relatively common and are probably due to normal anatomical variations in healthy eyes. We also introduce a simple anatomical artifact model based upon known anatomical variations to help distinguish these artifacts from actual glaucomatous damage. In section 3, we apply this model to an OCT-based method for detecting glaucoma that starts with an examination of the retinal nerve fiber layer (RNFL) p-map. While this method requires a judgment by the clinician, sections 4 and 5 describe automated methods that do not. In section 4, the simple model helps explain the relatively poor performance of commonly employed summary statistics, including circumpapillary RNFL thickness. In section 5, the model helps account for the success of an AI deep learning model, which in turn validates our focus on the RNFL p-map. Finally, in section 6 we consider the implications of OCT-based methods for the clinic, research, screening, and the development of AI models.

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression

Purpose

To assess the effects of local defects, segmentation errors, and improper image alignment on the performance of the commonly used optical coherence tomography (OCT) measure of progression, that is the change in global (average) circumpapillary retinal nerve fiber layer (cpRNFL) thickness (ΔG).

Methods

One hundred fifty eyes suspected of, or with, early glaucoma had OCT circle and cube scans obtained using eye tracking on two occasions at least 1 year apart. Statistical progression was defined by fixed values of ΔG (3-8 um) and quantile regression. For a reference standard, four authors identified 30 eyes as "likely progressed," and 61 eyes that "likely had not progressed" based on OCT reports from both baseline and follow-up tests.

Results

A ΔG criterion of 4 um had the best accuracy: 77%, with 5 false positive (8.2%) and 16 false negative (53%). A post hoc analysis of circular b-scans and OCT probability maps of these eyes indicated that segmentation errors and local progression accounted for most of these mistakes. Segmentation errors, although less common, were also present in true positives and true negatives.

Conclusions

Local defects and segmentation errors are the primary reasons for the poor performance of cpRNFL thickness G metric. Because these problems are difficult, if not impossible, to eliminate, the G metric should not be relied on in isolation for detecting glaucomatous progression.

Translational Relevance

Local defects and segmentation errors are easily identified by viewing OCT circumpapillary images, which should be part of the standard protocol for detecting glaucomatous progression.

Artificial intelligence and deep learning in glaucoma: Current state and future prospects

Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmology; this has naturally been translated to glaucoma-a progressive optic neuropathy characterized by retinal ganglion cell axon loss and associated visual field defects. In this review, we aim to discuss how AI may have a unique opportunity to tackle the many challenges faced in the glaucoma clinic. This is because glaucoma remains poorly understood with difficulties in providing early diagnosis and prognosis accurately and in a timely fashion. In the short term, AI could also become a game changer by paving the way for the first cost-effective glaucoma screening campaigns. While there are undeniable technical and clinical challenges ahead, and more so than for other ophthalmic disorders whereby AI is already booming, we strongly believe that glaucoma specialists should embrace AI as a companion to their practice. Finally, this review will also remind ourselves that glaucoma is a complex group of disorders with a multitude of physiological manifestations that cannot yet be observed clinically. AI in glaucoma is here to stay, but it will not be the only tool to solve glaucoma.

Optical Coherence Tomography Structural Abnormality Detection in Glaucoma Using Topographically Correspondent Rim and Retinal Nerve Fiber Layer Criteria

PURPOSE

This study evaluated the ability of topographically correspondent (TC) minimum rim width (MRW) and peripapillary retinal nerve fiber layer thickness (pRNFLT) criteria to detect optical coherence tomography (OCT) structural abnormality in glaucoma (GL) and glaucoma suspect (GLS) eyes.

DESIGN

Retrospective cross-sectional study.

METHODS

A total of 196 GL eyes, 150 GLS eyes, and 303 heathy eyes underwent pRNFL and 24 radial optic nerve head OCT imaging and manual correction of the internal limiting membrane, Bruch's membrane opening (BMO), and outer pRNFL segmentations. MRW and pRNFLT were quantified in 6 Garway-Heath or 12 30-degree (clock-hour) sectors. OCT abnormality for each parameter was defined to be less than the 5th percentile of the healthy eye distribution. OCT abnormality for individual eyes was defined using global, sectoral, and combined parameter criteria that achieved ≥95% specificity in the healthy eyes. TC combination criteria required the sectoral location of MRW and pRNFLT abnormality to be topographically aligned and included comMR (a previously reported TC combination consisting of MRW and pRNFLT parameter: [MRW + pRNFLT × (average MRW healthy eyes/average pRNFLT healthy eyes) MRW].

RESULTS

TC sectoral criteria (1 Garway-Heath MRW + corresponding Garway-Heath RNFLT), (one 30-degree MRW + any 1 corresponding or adjacent 30-degree pRNFLT), 30-degree and Garway-Heath comMR-TI and global comMR were the best performing criteria, demonstrating (96%-99% specificity), 86%-91% sensitivity for GL, 80%-84% sensitivity for early GL (MD ≥ -4.0 dB) and 93%-96% sensitivity for moderate-to-advanced GL (MD < -4.0 dB).

CONCLUSIONS

Clinically intuitive TC MRW and pRNFLT combination criteria identified the sectoral location of OCT abnormality in GL eyes with high diagnostic precision.

Evaluation of macular thickness and volume tested by optical coherence tomography as biomarkers for Alzheimer's disease in a memory clinic

Building on previous studies that report thinning of the macula in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) patients, the use of optical coherence tomography (OCT) has been proposed as a potential biomarker for AD. However, other studies contradict these results. A total of 930 participants (414 cognitively healthy people, 192 with probable amnestic MCI, and 324 probable AD patients) from a memory clinic were consecutively included in this study and underwent a spectral domain OCT scan (Maestro, Topcon) to assess total macular volume and thickness. Macular width measurements were also taken in several subregions (central, inner, and outer rings) and in layers such as the retinal nerve fiber (RNFL) and ganglion cell (CGL). The study employed a design of high ecological validity, with adjustment by age, education, sex, and OCT image quality. AD, MCI, and control groups did not significantly vary with regard to volume and retinal thickness in different layers. When these groups were compared, multivariate-adjusted analysis disclosed no significant differences in total (p = 0.564), CGL (p = 0.267), RNFL (p = 0.574), and macular thickness and volume (p = 0.380). The only macular regions showing significant differences were the superior (p = 0.040) and nasal (p = 0.040) sectors of the inner macular ring. However, adjustment for multiple comparisons nullified this significance. These results are not supporting existing claims for the usefulness of macular thickness as a biomarker of cognitive impairment in a memory unit. OCT biomarkers for AD should be subject to further longitudinal testing.