An automatic approach to fetal magnetic resonance image segmentation using 2D U-Net Architecture
Bibliographic record
Abstract
Fetal magnetic resonance imaging is imperative to diagnosing and treating fetal disorders because it is the most effective imaging modality given its high spatial and coarse resolutions and soft-tissue contrast. Segmentation is a required step performed by radiologists to aid clinicians to treat and track disease in utero. Segmentation is followed by biometric calculations to determine the weight of the fetus for diagnosing intrauterine growth restrictions, fetal brain and cardiac abnormalities, and other fetal and congenital disorders. However, manual segmentations are time-consuming, inaccurate, and dependent on the skills of the operator. An automatic segmentation method can mitigate these drawbacks and improve maternal-fetal health by reducing wait times for treatment and improving the accuracy and standardization of segmentations. This thesis presents an automatic algorithm using the successful deep learning model U-Net, to segment the whole fetus from and MRI of the maternal abdomen with 86.70% Dice Coefficient accuracy. This is the first convolutional neural network applied for this task and outperforms other models used for similar tasks. This novel algorithm can be applied in both clinical and research settings as pre-processing pipelines to segment the whole fetus from maternal MR images.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".