Automatic segmentation of placenta for quantitative ultrasound analysis
Bibliographic record
Abstract
Quantitative ultrasound (QUS) holds promise for non-invasive placental tissue characterization and disease detection, yet its clinical application is hindered by the effort required for placental segmentation to identify a region-of-interest (ROI) for calculation. Manual segmentation is time-consuming and prone to variability due to the irregular boundaries of the placental tissue. This study aims to develop an automatic placental identification and segmentation model to facilitate QUS integration into clinical practice, alleviate clinician workload, and enable real-time feedback on QUS quality. We employed Mask R-CNN within the Detectron2 framework to automate placental segmentation using a dataset of 149 B-mode ultrasound images annotated by a medical image specialist (M.D.) covering various trimesters. Inference on a validation subset (30 images) yielded an average Dice Similarity Coefficient (DSC) of 0.863. Our model gave quality predictions for a majority of the test images with 57% of segmentations achieving DSC ≥ 0.85 and 40% with DSC ≥ 0.90. The model’s low inference time of approximately 55 ms per iteration supports near real-time QUS processing. Future work will focus on expanding the dataset, optimizing the loss function, and addressing edge effects on QUS to further enhance segmentation quality.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".