Development of OCTA Automated Segmentation and Measurement Tools for Assessment of Cerebrovascular and Neural Health in Aging Populations
Bibliographic record
Abstract
Cerebral small vessel disease (CSVD) is one of the most commonly occurring vascular disease in the older population. Research has shown that the emergence of cerebrovascular disease has a profound effect on the changes that occurs in retinal vasculature. Optical coherence tomography angiography (OCTA) is a non-invasive, high resolution imaging modality used to extract images of retinal vasculature. However, manual segmentation of these images requires experienced clinical expert and is highly subjective to the individual's background. Therefore, there is a need of developing an automated segmentation technique for OCTA images. This research proposes a novel approach for automatic segmentation of OCTA images. The OCTA scans in this study involved individuals from two groups, older individuals whose scans were obtained from a publicly available dataset ROSE-1 [1], and OCT images of younger individuals that were acquired in the lab at University of Ottawa called NCM images. The proposed approach comprises of pixel-level and centerline level segmentation of OCTA images using Attention UNet model of different depths and merging these images to form a fully segmented superficial vascular complex (SVC) OCTA image. The experimental results demonstrates that the proposed approach (for fully segmented SVC OCTA) provides an accuracy of 0.9188, average dice of 0.7853, kappa score of 0.7354, G-mean score of 0.847 and balance accuracy of 0.85518. The trained pixel-level segmentation model was again used to segment NCM images, and the resulting segmented image was overlapped over the original image and studied. Subsequently, pixel-level segmented images were used to extract vascular features from retina such as vessel density, vessel length density, vessel perimeter index and vessel mean diameter for both the groups. It was found that there is a decrease in mean diameter of blood vessels (p-value < 0.05) in older individuals as compared to the younger group while the other parameters were not statistically significant (p-value > 0.05).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".