Diagnosis of Purtscher Retinopathy from Spectral-Domain OCT Images Using Panoptic Segmentation Technique
Bibliographic record
Abstract
Purtscher Retinopathy (PR) is a severe vision-threatening disorder that is frequently connected with trauma, severe pancreatitis, or connective tissue diseases.Conventional diagnostic techniques depend on fluorescein angiography and clinical examination, both of which can be intrusive and may not always yield conclusive results.Spectral-Domain Optical Coherence Tomography (SD-OCT) is a non-invasive imaging technique that produces high-resolution cross-sectional images of the retina and allows for accurate evaluation of retinal layers and illnesses.The proposed method implements a panoptic segmentation algorithm to detect PR using SD-OCT image data in an unsupervised manner.The Panoptic Feature Pyramid Network (FPN) detects and categorizes clinical indications of PR using instance and semantic segmentation.To detect additional challenging factors, a deep Convolutional Neural Network (CNN) is combined with encoder-decoder structures and instance segmentation networks.When it came to recognizing and classifying retinal disorders connected to PR, the proposed approach showed excellent accuracy.The performance of the proposed method was assessed using quantitative measures such as the Intersection over Union (IoU), Dice coefficient, pixel accuracy, precision, recall, and F1 score.This approach offers a non-invasive and efficient tool for the early detection of PR, allowing for timely management and better patient outcomes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".