Optimising neural networks for perforator detection in DIEP flap breast reconstruction using dynamic infrared thermography
Bibliographic record
Abstract
Breast reconstruction following mastectomy is increasingly performed, with Deep Inferior Epigastric artery Perforator (DIEP) flap surgery considered the gold standard. Accurate preoperative perforator selection is vital to minimize complications and operative time. While computed tomography angiography (CTA) remains the clinical reference, drawbacks including radiation, contrast use, and cost motivate exploration of non-invasive alternatives. Dynamic Infrared Thermography (DIRT) offers a low-cost, radiation-free method but still lacks automation. This study evaluates deep learning for automated perforator detection in DIRT. A dataset of 50 time-lapse thermograms from five patients was acquired using various cooling methods and validated through leave-one-out cross-validation (LOOCV). Two neural network architectures were compared: a standard U-Net and a modified U-Net (mU-Net) from prior work. U-Net consistently outperformed mU-Net. Across LOOCV folds, U-Net achieved a mean weighted Dice loss of 0.42 ± 0.09, sensitivity of 0.87 ± 0.08, and precision of 0.82 ± 0.14. On an independent test patient, sensitivity remained high (0.88) but precision decreased (0.58). The mU-Net failed to converge (validation loss 0.87 ± 0.02), producing uniform segmentations. These findings demonstrate that U-Net is a robust tool for automated perforator detection in DIRT, though false positives highlight the need for larger datasets and further optimisation before clinical use.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".