Ganglion cell-inner plexiform layer measurements derived from widefield compared to montaged 9-field optical coherence tomography

Exploring the relationship between 24-2 visual field and widefield optical coherence tomography data across healthy, glaucoma suspect and glaucoma eyes

PURPOSE

To utilise ganglion cell-inner plexiform layer (GCIPL) measurements acquired using widefield optical coherence tomography (OCT) scans spanning 55° × 45° to explore the link between co-localised structural parameters and clinical visual field (VF) data.

METHODS

Widefield OCT scans acquired from 311 healthy, 268 glaucoma suspect and 269 glaucoma eyes were segmented to generate GCIPL thickness measurements. Estimated ganglion cell (GC) counts, calculated from GCIPL measurements, were plotted against 24-2 SITA Faster visual field (VF) thresholds, and regression models were computed with data categorised by diagnosis and VF status. Classification of locations as VF defective or non-defective using GCIPL parameters computed across eccentricity- and hemifield-dependent clusters was assessed by analysing areas under receiver operating characteristic curves (AUROCCs). Sensitivities and specificities were calculated per diagnostic category.

RESULTS

Segmented linear regression models between GC counts and VF thresholds demonstrated higher variability in VF defective locations relative to non-defective locations (mean absolute error 6.10-9.93 dB and 1.43-1.91 dB, respectively). AUROCCs from cluster-wide GCIPL parameters were similar across methods centrally (p = 0.06-0.84) but significantly greater peripherally, especially when considering classification of more central locations (p < 0.0001). Across diagnoses, cluster-wide GCIPL parameters demonstrated variable sensitivities and specificities (0.36-0.93 and 0.65-0.98, respectively), with the highest specificities observed across healthy eyes (0.73-0.98).

CONCLUSIONS

Quantitative prediction of VF thresholds from widefield OCT is affected by high variability at VF defective locations. Prediction of VF status based on cluster-wide GCIPL parameters from widefield OCT could become useful to aid clinical decision-making in appropriately targeting VF assessments.

Characterisation of the normal human ganglion cell-inner plexiform layer using widefield optical coherence tomography

PURPOSE

To describe variations in ganglion cell-inner plexiform layer (GCIPL) thickness in a healthy cohort from widefield optical coherence tomography (OCT) scans.

METHODS

Widefield OCT scans spanning 55° × 45° were acquired from 470 healthy eyes. The GCIPL was automatically segmented using deep learning methods. Thickness measurements were extracted after correction for warpage and retinal tilt. Multiple linear regression analysis was applied to discern trends between global GCIPL thickness and age, axial length and sex. To further characterise age-related change, hierarchical and two-step cluster algorithms were applied to identify locations sharing similar ageing properties, and rates of change were quantified using regression analyses with data pooled by cluster analysis outcomes.

RESULTS

Declines in widefield GCIPL thickness with age, increasing axial length and female sex were observed (parameter estimates -0.053, -0.436 and -0.464, p-values <0.001, <0.001 and 0.02, respectively). Cluster analyses revealed concentric, slightly nasally displaced, horseshoe patterns of age-related change in the GCIPL, with up to four statistically distinct clusters outside the macula. Linear regression analyses revealed significant ageing decline in GCIPL thickness across all clusters, with faster rates of change observed at central locations when expressed as absolute (slope = -0.19 centrally vs. -0.04 to -0.12 peripherally) and percentage rates of change (slope = -0.001 centrally vs. -0.0005 peripherally).

CONCLUSIONS

Normative variations in GCIPL thickness from widefield OCT with age, axial length and sex were noted, highlighting factors worth considering in further developments. Widefield OCT has promising potential to facilitate quantitative detection of abnormal GCIPL outside standard fields of view.

A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation

Deep learning methods have enabled a fast, accurate and automated approach for retinal layer segmentation in posterior segment OCT images. Due to the success of semantic segmentation methods adopting the U-Net, a wide range of variants and improvements have been developed and applied to OCT segmentation. Unfortunately, the relative performance of these methods is difficult to ascertain for OCT retinal layer segmentation due to a lack of comprehensive comparative studies, and a lack of proper matching between networks in previous comparisons, as well as the use of different OCT datasets between studies. In this paper, a detailed and unbiased comparison is performed between eight U-Net architecture variants across four different OCT datasets from a range of different populations, ocular pathologies, acquisition parameters, instruments and segmentation tasks. The U-Net architecture variants evaluated include some which have not been previously explored for OCT segmentation. Using the Dice coefficient to evaluate segmentation performance, minimal differences were noted between most of the tested architectures across the four datasets. Using an extra convolutional layer per pooling block gave a small improvement in segmentation performance for all architectures across all four datasets. This finding highlights the importance of careful architecture comparison (e.g. ensuring networks are matched using an equivalent number of layers) to obtain a true and unbiased performance assessment of fully semantic models. Overall, this study demonstrates that the vanilla U-Net is sufficient for OCT retinal layer segmentation and that state-of-the-art methods and other architectural changes are potentially unnecessary for this particular task, especially given the associated increased complexity and slower speed for the marginal performance gains observed. Given the U-Net model and its variants represent one of the most commonly applied image segmentation methods, the consistent findings across several datasets here are likely to translate to many other OCT datasets and studies. This will provide significant value by saving time and cost in experimentation and model development as well as reduced inference time in practice by selecting simpler models.