Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation
Bibliographic record
Abstract
Spectral-domain Optical Coherence Tomography (SD-OCT) is a critical tool in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate segmentation of sub-retinal layers is essential for diagnosing and monitoring retinal diseases. While manual segmentation by clinicians is the gold standard, it is subjective, time-intensive, and impractical for large-scale use. This study introduces an automated segmentation algorithm based on graph theory, utilizing a shortest-path graph-search technique to delineate seven intra-retinal boundaries. The algorithm incorporates a region of interest (ROI) selection to enhance efficiency, achieving a mean computation time of 0.93 s on standard systems suitable for real-time clinical applications. Image denoising was evaluated using Gaussian and wavelet-based filters. While wavelet-based denoising improved accuracy to some extent, its increased computation time (~10 s/image) was the trade-off. The intra-retinal layer thicknesses computed by the segmentation algorithm was consistent with previous studies and demonstrated high accuracy with respect to manual segmentation, thus indicating clinical relevance. Future research will explore integrating machine learning to improve robustness across diverse retinal pathologies, enhancing the algorithm’s applicability in clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".