Automated Segmentation of Optic Nerve Head Structures With Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To quantify and characterize the difference between automated and manual segmentation of optic nerve head structures with spectral-domain optical coherence tomography (SD-OCT). METHODS: Optic nerve head radial scans in 107 glaucoma patients and 48 healthy controls were conducted with SD-OCT. Independent segmentations of the internal limiting membrane (ILM) and Bruch's membrane opening (BMO) were performed manually with custom software and with an automated algorithm in each radial scan. The minimum distance between BMO and ILM, termed BMO-minimum rim width (BMO-MRW) was calculated with each segmentation method. Absolute differences between automated and manual segmentations of ILM (ΔILM) and BMO (ΔBMO), and the resulting computation of BMO-MRW (ΔBMO-MRW) were computed. Finally, the relationship between image quality score and ΔILM and ΔBMO was explored. RESULTS: The median (interquartile range, IQR) ΔILM was 8.9 (6.5, 13.4) μm in patients and 7.3 (5.3, 9.9) μm in controls. The corresponding values for ΔBMO were 11.5 (6.6, 22.1) μm and 12.4 (6.8, 25.4) μm. Subject-averaged ΔILM was higher in patients than controls (P < 0.01); however, mean ΔBMO was not (P = 0.09). The median (IQR) subject-averaged absolute ΔBMO-MRW was 13.4 (10.6, 16.8) μm in patients and 12.1 (10.0, 16.8) μm in controls and not statistically different (P = 0.21). Mean image quality score was statistically higher in controls than patients (P = 0.03) but not related to subject-averaged ΔILM or ΔBMO. CONCLUSIONS: In individual scans, the median difference in ILM and BMO segmentations was <2 and <3 image pixels, respectively. There were no differences between patients and controls in ΔBMO-MRW.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".