Quantifying Variability in Longitudinal Peripapillary RNFL and Choroidal Layer Thickness Using Surface Based Registration of OCT Images
Bibliographic record
Abstract
PURPOSE: To assess within-subject variability of retinal nerve fiber layer (RNFL) and choroidal layer thickness in longitudinal repeat optical coherence tomography (OCT) images with point-to-point measurement comparison made using nonrigid surface registration. METHODS: Nine repeat peripapillary OCT images were acquired over 3 weeks from 12 eyes of 6 young, healthy subjects using a 1060-nm prototype swept-source device. The RNFL, choroid and the Bruch's membrane opening (BMO) were segmented, and point-wise layer thicknesses and BMO dimensions were measured. For each eye, the layer surfaces of eight follow-up images were registered to those of the baseline image, first by rigid alignment using blood vessel projections and axial height and tilt correction, followed by nonrigid registration of currents-based diffeomorphisms algorithms. This mapped all follow-up measurements point-wise to the common baseline coordinate system, allowing for point-wise statistical analysis. Measurement variability was evaluated point-wise for layer thicknesses and BMO dimensions by time-standard deviation (tSD). RESULTS: The intraclass correlation coefficients (ICCs) of BMO area and eccentricity were 0.993 and 0.972, respectively. Time-mean and tSD were computed point-wise for RNFL and choroidal thickness and color-mapped on the baseline surfaces. tSD was less than two coherence lengths of the system 2ℓ = 12 μm at most vertices. High RNFL thickness variability corresponded to the locations of retinal vessels, and choroidal thickness varied more than RNFL thickness. CONCLUSIONS: Our registration-based end-to-end pipeline produced point-wise correspondence among time-series retinal and choroidal surfaces with high measurement repeatability (low variability). Blood vessels were found to be the main sources contributing to the normal variability of the RNFL thickness measure. The computational pipeline with a measurement of normal variability can be used in future longitudinal studies to identify changes that are above the threshold of normal point-wise variability and track localized changes in retinal layers in high spatial resolution. TRANSLATIONAL RELEVANCE: Using the registration-based approach presented in this study, longitudinal changes in retinal and choroidal layers can be detected with higher sensitivity and spatial precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".