A Real-Time Image Stitching Framework for Fetoscopic Field-of-View Expansion
Bibliographic record
Abstract
Twin-to-twin transfusion syndrome is a serious prenatal condition whose treatment employs the use of a fetoscope and optical laser for targeted vessel ablation. During surgery, challenges such as a limited field-of-view and inconsistent lighting can be addressed by field-of-view expansion. As foundation, we draw upon a state-of-the-art method that utilizes a U-Net segmentation model to mitigate visible in utero inconsistencies. We adapt this method for real-time performance by implementing a feature-based stitching algorithm. The proposed framework includes a loop closure and map correction procedure to help reduce the accumulated drifting error. During evaluation on simulated fetoscopic movements, the chosen feature extractor, SIFT, permits an average homography estimation time of 0.07s and a failure rate of 4%. Homography parameters such as scaling, translation, and rotation are isolated to identify obstacles caused by certain camera movements. The framework is evaluated using two phantom placentas in different environmental conditions and submerged underwater. Qualitative and quantitative results are presented as well as an analysis of the map correction procedure. These evaluations help recognize limitations of the framework that can be investigated and addressed in future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".