Multi-task deep learning for segmentation and landmark detection in obstetric sonography
Bibliographic record
Abstract
The utilization of point-of-care ultrasound (POCUS) has been on rise in recent years, followed by a growing need for comprehensive, compact obstetrics analysis software systems. The accurate computerized assessment of obstetric ultrasound (US) is a challenging task due to the noisy nature of US images and presence of complex anatomies. In this work, we propose a multi-branch deep learning architecture to identify multiple anatomies in obstetric sonography through segmentation and landmark detection. The multi-task deep model is trained to segment the uterus and gestational sac regions, and to localize the position of landmark points denoting the crown and rump of the fetus. We conduct experiments with varying sizes of models, presenting a trade-off between accuracy and efficiency. Our larger models reach average Dice scores of 90% and 91% for segmenting uterus and gestational sac, respectively, and have 1.64 mm average length error for the fetus crown-rump length (CRL) measurement. Furthermore, we present choices for smaller model sizes suitable for integration in portable POCUS devices with limited computing capacities. The smallest model with the most efficient run-time has only about 167.3k parameters, where compared to the larger models, the Dice score is reduced by 4.1% and 6.7% for uterus and gestational sac segmentation, respectively, and the CRL length error is increased by 0.42 mm. This is while the smallest model has 98.8% reduced model size and could smoothly run on naive mobile devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".