Semi-automatic segmentation of preterm neonate ventricle system from 3D ultrasound images
Bibliographic record
Abstract
3D Ultrasound (US) has been developed recently to image the intracranial ventricular system of pre-term neonates in order to monitor these patients for intraventricular hemorrhage (IVH) and the resultant dilatation of the ventricles. 3D US is capable of providing volumetric ventricle measurements, compared to clinically used 2D US, relying on linear measurements from a single slice, and visual and quantitative estimates to determine the severity of ventricular dilatation. In this work, we propose a convex optimization-based segmentation approach for 3D US images of the cerebral ventricles in preterm neonates with IVH. The proposed semi-automatic segmentation method makes use of the latest development in convex optimization techniques supervised by user interactive information. Experiments using 25 3D US images of 5 patients (5 time points for each subject) show that our proposed approach yielded a mean DSC of 78.9% compared to a manually contoured surface. This GPU-implemented semi-automated approach reduced the time required per segmentation by 1200% (mean times: 2.5 vs. 30 minutes). In addition, the intra-observer variability experiments showed that the variability introduced by the user initialization is small in terms of DSC, demonstrating a low intra-user variability.
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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".