Noise-Free One-Cardiac-Cycle Oct Videos for Local Assessment of Retinal Tissue Deformation
Bibliographic record
Abstract
The analysis and quantification of retinal tissue biomechanics is important for understanding the pathophysiology of glaucoma. The dynamics of anatomical changes can uncover information that is not available in static Optical Coherence Tomography (OCT) images. However, noise in OCT images hampers detailed analysis of time series with high levels of accuracy. In order to produce good quality videos of the retina, we reduced video acquisitions of approximately thirty seconds to one cardiac cycle by synchronizing the acquisition of OCT images with the measurement of the patient’s pulse. After spatial registration of the images, we phase wrapped each measurement using the heart frequency to average the frames of the video which correspond to the same instant in the cardiac cycle. These videos with a duration of a single cycle allow a precise analysis of the movement of the tissues of the retina. Using the proposed workflow on the OCT acquisitions of 15 patients (video length of 30 seconds), the CNR and SNR, which measure signal quality in the images, and the MMI, which measures registration accuracy, all increased significantly. The resulting videos allow local tissue displacement to be seen more clearly and analysed more precisely than with standard image registration methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".