Semi-supervised deep learning based 3D analysis of the peripapillary region
Bibliographic record
Abstract
Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".