Automated endometrial segmentation, thickness measurement and pattern prediction on uterine ultrasound images
Bibliographic record
Abstract
To develop an AI-based model for automated identification, thickness measurement, and pattern classification of endometrial tissues in uterine ultrasound images. We utilized a multi-stage model building and training approach with different methods for segmentation, thickness measurement, and pattern assignment. 5985 unique annotated ultrasound images were utilized in model training and validation. The annotated images were randomly divided into three groups (training data, validation data, and test data) at a 70%/10%/20% ratio. 4787 images were used to develop the model; 1198 images were used to evaluate performance. Identification of the endometrium was done utilizing direct object detection and instance segmentation systems with single stage object detectors (YOLO v8). A principal component analysis approach was adapted to determine endometrial thickness. We also trained computational models to assign an endometrial pattern of either ‘trilaminar’ or ‘homogenous’. Intersection over union (IoU), confusion matrices, and error calculations were conducted to assess the model’s proficiency and accuracy. The endometrial segmentation model performed at 98% accuracy on the confusion matrix and 92.8% of test data had an intersection over union (IoU) value > 0.75. The endometrial thickness measurement achieved mean absolute error rates of: ± 0.89 mm in length, ± 2.81 mm in position (along the perpendicular-to-lumen axis), and 5.5-degrees in orientation relative to the lumen. Automatic assignment of pattern as either ‘trilaminar’ or ‘homogenous’ achieved a 92% accuracy rate. A novel automated method for routine endometrial thickness and pattern assessment is demonstrated. We report a first-of-its kind method for automated endometrial pattern assignment (homogenous / triple-line) on uterine ultrasound 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".