A new method for quantification of retinal blood vessel characteristics
Bibliographic record
Abstract
Uniform and quantitative grading of retinal vessel characteristics are replacing subjective and qualitative schemes. However clinically accurate blood vessel extraction is very important. The tortuosity of these vessels is an important metric to study the curvature variations in normal and diseased eyes. In this study we provide a new unsupervised and fully automated approach for studying curvature variation of the blood vessels. We then pro- vide tortuosity quantification of these extracted vessels. In this study we used optical coherence tomography angiographic fundus images of dimensions 420x420 pixels corresponding to 6mm x 6mm were used in this study. We focused on the central circular 210x210 pixel region around the foveal avascular zone (FAZ) for tortuosity quantification. Our segmentation approach starts with a 3mm x 3mm central circular region extraction surrounding the FAZ. We then use a multi-scale, multi-span line detection filter to smoothen out the high noise in the background and at the same time increase the intensity of target vessels. This is followed by a K-means procedure to filter out the noise and target vessels into two categories. Next steps are morphological closing and noise removal and iterative erosion of pixels to skeletonize the vessels. The final extracted vessels are of the form of single pixel piecewise continuous fragments. These are finer than human annotations and at the same time free of noise. We then provide accurate standard tortuosity measures - Distance Measure, Inflection Points, Turning Points, etc. for these OCTA images using the extracted vessels through mathematical modelling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".