Transfer Learning with U-Net type model for Automatic Segmentation of Three Retinal Layers In Optical Coherence Tomography Images
Bibliographic record
Abstract
Retinal layer analysis on OCT images is a standard procedure used by ophthalmologists to diagnose various diseases. Due to a large number of generated OCT images for each patient, a manual image analysis can be time-consuming and error-prone, which can consequently affect the timeliness and quality of the diagnosis. Therefore, in recent years, a variety of methods, based prevalently on deep learning, have been proposed for the automatic segmentation of retinal layers. In our study, the U-Net type model with a ResNet based encoder, pretrained on ImageNet dataset is utilized. In addition, the model is combined with postprocessing step to obtain segmented layer boundaries. The modified versions of U-Net type model have already been applied to various non-medical imaging segmentation tasks, achieving outstanding results. To investigate whether the pretrained U-Net type model contributes to improvement of retinal layer segmentation, two models are trained and validated on 23 volumes of OCT images with age related macular degeneration (AMD): the U-Net model with pretrained ResNet34 encoder on ImageNet dataset and the original U-Net model, trained from the scratch. The one-sided Wilcoxon signed-rank test has shown that the pretrained U-Net type model outperforms the original U-Net model for segmenting three regions bounded by four layer boundaries.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".